Invisible Work
The Skills AI Can’t See
Now accepting keynotes for Q4 2025 / Q1 2026
As 2025 comes to a close and leaders everywhere prepare for a year of acceleration, I’m opening bookings for my keynote The SuperSkills Era: Thriving in the Age of AI. These 60-minute executive sessions draw on the research and frameworks from my upcoming book and focus on the human capabilities every leadership team will need as AI reshapes work. Designed for boards and senior teams, they can be delivered in person or virtually across the UK, Europe and globally. Slots are now open for Q4 2025 and Q1 2026 – a powerful way to reset thinking for the year ahead and future-proof your organisation. [Enquire here to book your session].
In addition to this newsletter, I recommend some other great ones. All free. Check them out here.
Friends,
Lydia lost her job to AI. Not because she had failed, but because her competence had become too easy to automate.
Her spreadsheets were flawless, her reports precise, her reliability unquestioned. Then her company installed new tech that performed her tasks in minutes. The definition of value had changed. In my discussions across industries, these kinds of changes are repeating. What can be written into code is being written into code. Tasks that were once visible and measurable are now executed by machines. What remains is harder to see and even harder to teach. Non-coders are coding. Some roles are now empowered to do more, while others are out of a job.
For most of modern history, skill left evidence. You could watch an accountant balance books, a carpenter shape wood, a designer sketch a layout. Competence was tangible. Today the blinking cursor and the glow of a screen hides both process and person. The craft of work has vanished from view, replaced by output that looks effortless and costs less.
AI performs the surface of work. It calculates, predicts and produces at speed. Yet the surface was never the whole story. Behind every report or design sat countless human judgements: how to frame a question, where to look for error, when to stop refining. Those decisions gave the work its integrity. They are the things automation still struggles to reproduce.
The half-life of skill keeps shrinking. In the 1980s a qualification might serve for thirty years. By the 1990s it lasted a decade. Today most technical knowledge fades within five. Soon tch skills will have a half life of one year, based on my estimation. Expertise has become a subscription service, renewed every time the system updates.
As the visible tasks disappear, the invisible ones decide who thrives. These are not soft skills. They are the operating system of human work: judgement, empathy, curiosity, discernment. Judgement decides what matters when data floods the room. Empathy hears tone, not only words, and keeps teams aligned. Curiosity asks questions no algorithm knows to ask. Discernment weighs all of the above before acting.
Such abilities cannot be downloaded. They grow through conversation, reflection and the long apprenticeship of experience. In a culture obsessed with efficiency they are easy to overlook, until something breaks. Algorithms excel at prediction; humans excel at interpretation. Machines recognise what has happened before and project it forward. People notice what has never happened and imagine what could.
Automation removes friction, yet friction is where understanding lives. A designer wrestling with a problem, a teacher waiting for a hesitant student, a doctor explaining uncertainty to a patient; those moments of pause are where empathy and meaning form. Technology has made work faster. It has also made depth optional.
After months of uncertainty, Lydia found work again. This time her role was not in the spreadsheets but in the space between data and decision. She became an analyst who helped teams grasp what the numbers meant rather than what they said. She learned to sit with ambiguity longer than others and to spot patterns the metrics missed. Her new skill was attention itself.
She no longer measured her worth in how much she produced but in how clearly she could see. That shift, from output to insight, is becoming the defining move of the new economy.
For decades organisations measured contribution by visibility: reports filed, hours logged, slides delivered. Thinking, listening, deciding, all forms of invisible work, were treated as background noise. Yet it is precisely that unseen effort that holds complex systems together. As automation spreads, value moves from efficiency to interpretation. The best leaders will not be those who master every tool but those who know when tools distort the picture. They will build environments where human judgement is amplified, not automated away. Technology can handle repetition. Only humans can hold ambiguity.
The next generation of work will sit at the boundary between human and machine. New roles are already appearing: AI editor, prompt architect, sense-maker, ethicist, trust designer. Their purpose is not to code faster but to turn complexity into clarity and intelligence into impact. Automation has taken much, yet it offers a strange gift. By stripping out routine, it forces us to rediscover the parts of work that make us human: imagination, care, connection and meaning. It forces to value the pause, having an open mind, and saying - let me think about that some more.
Lydia’s story is both warning and invitation. The warning is that no skill stays visible for long. The invitation is to strengthen the ones that never disappear. The future will not ask what we know. It will ask what we notice.
Stay Curious - and don’t forget to be amazing,
PS 10 questions to answer before you die
Here are my recommendations for this week:
One of the best tools for providing excellent reading material and articles for your week is Refind. It’s a fantastic tool for staying ahead with “brain food” relevant to you and offers serendipitous discoveries of great articles you might have missed. You can dip in and sign up for weekly, daily, or something in between - what’s guaranteed is that the algorithm delivers only the best articles in your chosen area. It’s also free. Highly recommended. Sign up now.
Now
This is probably the most important AI document you haven’t read... and i’m currently in that rabbit hole trying to figure it out. Emad Mostaque (founder of Stability AI), the one who says some smart things in interviews, has published a book, and a sort of open-source whitepaper/manifesto called the Intelligent Internet. Together, they’re an ambitious attempt yet to sketch the post-AI world. Noone I have asked recently has read it. It’s 99c on Kindle or free at this link. It’s a must read to understand what the future might be like.
What is fibermaxxing? - “Fibermaxxing” is the latest TikTok wellness trend focused on deliberately increasing fibre intake to improve gut, metabolic and hormonal health. Experts recommend around 30 grams of fibre daily from diverse plant sources. Combining soluble and insoluble fibre supports digestion, blood sugar and cholesterol. Easy boosts include seeds, wholegrains, herbs and psyllium husk.
Dan Shipper argues business “laws” are heuristics. Treat company building like a language model: learn by repeated next-move prediction in real contexts. Prioritise wayfinding over rigid plans, finish full cycles, buy time, and develop gut feel. Use principles as anecdotes, not edicts, and balance theory with lived experience and self-knowledge.
The Dubai chocolate craze is now about much more than bars - Dubai chocolate, born at Fix Chocolatier in 2021, blends thick milk-chocolate shells with creamy pistachio-tahini and crunchy kadayif. The viral TikTok hit has spawned croissants, milkshakes and cakes, surged pistachio demand, and reached major US retailers. Premium-priced yet booming, pistachio-filled chocolate sales rose 1,234% as overall chocolate slipped in 2025.
Yes ChatFishing. is a thing - ‘I realised I’d been ChatGPT-ed into bed’: how ‘Chatfishing’ made finding love on dating apps even weirder
Next
Good Morning & Good Luck - Tomasz Tunguz reflects on how AI now mediates nearly all his news consumption, from ChatGPT to Gemini. As users abandon web browsing for AI summaries, traffic plummets and traditional discovery fades. AI, he argues, is becoming the new mass media—centralised yet personalised—replacing the fragmented web with probabilistic information networks.
I shouldn’t be saying this about the AI jobs armageddon, but the data compels me.
Sam Altman (yes, that Sam Altman) just said that maybe our jobs aren’t “real work” anyway. His argument? A farmer from 50 years ago would look at what we do and think we’re “playing a game to fill our time.” The man building the technology that might replace your job is telling you your job was fake to begin with.
Forget the ‘AI Bubble’. Are We Actually in an Everything Bubble? - Economists warn that soaring prices across stocks, housing, food and gold suggest an “everything bubble,” fuelled by AI hype, trade tensions and wealth concentration. While strong corporate profits sustain optimism, rising debt, tariffs and inequality expose fragility. Analysts advise rebalancing portfolios as speculation grows and markets risk a sharp correction.
When Face Recognition Doesn’t Know Your Face Is a Face - Facial-recognition and selfie-verification systems are increasingly locking out people with facial differences (birthmarks, craniofacial conditions, scars), misclassifying their faces or failing “liveness” checks. WIRED documents denials at DMVs, passport gates, credit bureaus, banks, apps and age-verification sites, echoing lifelong stigma with a digital barrier. Advocacy groups (Face Equality International) say datasets and design exclude diverse faces, and support lines rarely offer workable alternatives. Experts urge mandated fallback options (human review, non-biometric routes), inclusive datasets, accessible UX, and clear escalation paths. Vendors cite training and accessibility efforts, but affected users report slow progress and repeated humiliation, highlighting systemic bias in “face-as-key” infrastructure.
The Most Transformational Growth Companies In Digital Learning and Workforce Skills - GSV evaluated 3,000+ global companies using a proprietary rubric of revenue scale, revenue growth, user reach, geographic diversification, and margins profile to derive the GSV 150. For the first time, publicly listed companies are also included, now capturing all EdTech companies that are experiencing top-line growth and are at a scale of minimum double-digit million USD in revenue. The collective 150 companies top $50 billion in annual revenue, reach over 3 billion learners, and are increasingly profitable, with two-thirds being cash flow profitable and their collective EBITDA score now 14% higher compared to 2025.
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So timely. 'What remains' truly makes me think.