Before the article, an exciting newsletter update…

Workforce Futurist Newsletter Update

Since September last year, Matteo Cellini (Work3 Newsletter) and I have published 28 weekly articles and hosted a webinar together.

We’re proud to share that our combined newsletters now have over 31,000 subscribers across 163 countries—and we’re incredibly grateful for your support 🙏.

To streamline our content and avoid duplication, we’ve decided to consolidate into one newsletter. From next week, we’ll be publishing exclusively from Work3 Newsletter.

📢 What this means for you
If you’re a Workforce Futurist subscriber, there’s nothing you need to do—we’ll automatically move you to the Work3 Newsletter, and your next article will arrive from a different email address.

We’re also working on exciting new partnerships and community events this year, and we’ll keep you updated.

Thanks for being part of this journey—see you next week!

Being Human

Technology feels like it’s reshaping the world in ways we can’t control.

The TechBros are swinging chainsaws—cutting jobs, flooding social media with AI-generated noise. Apple’s market cap is now larger than the GDP of all but seven countries.

In response, a tech resistance is emerging:

• Governments are banning social media for kids. (Australia now restricts social media access for under-16s.)

• People are ditching WhatsApp for privacy-friendly alternatives like Signal and Telegram.

• The dumbphone movement is growing, as users swap their iPhones for old-school Nokias to escape digital addiction.

These resistance movements come from a real concern—that technology is making us less human, not more.

And yet, I don’t think the answer is to opt out entirely.

There have been five technology-led revolutions in that last 250 years, according to Carlota Perez. Before the Digital Age, we had the Age of Oil.

But Technology isn’t like Oil—it’s more powerful than that.

Oil powered industries; technology rewires decision-making, behavior, and even identity.

It can be used to exploit us, or to empower us.

We have a choice.

If we shape technology with intention, we can use it to solve human problems, build deeper connections, and expand what’s possible.

Here are seven ways technology is making us more human—not less.

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1️⃣ AI is Helping Us Understand Ourselves Like Never Before

For centuries, we’ve guessed at how we think, feel, and behave. Now, AI is revealing patterns in human psychology, decision-making, and well-being that we never could have seen before. We now have a treasure trove of data revealing how we truly behave-often contradicting what we report in surveys.

• AI-powered brain research is unlocking how memory, emotions, and burnout work.

• Wearables track focus, stress, and energy levels, helping people optimize their performance.

• AI can detect mental health struggles before we even realize we’re struggling.

🔹 Example: Woebot - an AI-driven mental health chatbot with strong clinical backing.

💡 The Future: Expect AI-driven personalized coaching tools that help us understand and improve our own behavior.

⚠️ Insights from this data should be regarded as a public good and not owned by companies or locked inside impenetrable academic firewalls.

2️⃣ AI and Platforms Are Shifting Us From Employees to Career Owners

Technology has unbundled work—we’re no longer limited to a single employer or job. AI and platforms are giving individuals more control than ever over when, where, and how they work.

• Freelancers use AI to find global clients.

• Substack, YouTube, and Patreon let creators monetize their audiences directly.

• AI copilots (ChatGPT, GitHub Copilot) supercharge productivity, making workers 10x faster.

🔹 Example: Braintrust - a decentralized talent network connecting freelancers with top companies, allowing them to keep 100% of their earnings.

💡 The Future: AI won’t necessarily replace workers—it will amplify those who know how to use it.

⚠️The platforms aren’t perfect – there are too many transaction taxes, friction, and digital fiefdoms to work around – but they are better than the alternative. (I’ve explored these challenges in more detail in my paper on user-owned platforms using decentralised technology)

3️⃣ AI is Helping Us Find the Right People—For Work, Love, and Life

We underestimate how much AI is already shaping human relationships. It’s not just automating tasks—it’s connecting us with people who share our interests, skills, and values.

• AI-powered job-matching helps professionals find meaningful careers.

• Dating apps use AI to predict compatibility more effectively than humans.

• Freelance platforms match people with ideal clients and collaborators.

🔹 Example: Lunchclub - facilitates professional networking by connecting individuals with similar interests for virtual or in-person meetings.

💡 The Future: AI-powered ‘life assistants’ will curate the best people for us to meet, collaborate with, and learn from.

4️⃣ Some People Will Thrive in Digital Worlds More Than in the Physical One

The Loneliness Epidemic might not be real, but more people prefer to be physically alone more of the time and technology is offering alternative ways to connect.

• VR therapy is helping people with social anxiety interact in safe environments.

• Virtual workspaces (e.g., Gather.town) are replacing traditional office dynamics.

• AI-generated worlds (Metaverse, spatial computing) are allowing people to design their ideal work and social spaces.

🔹 Example: Virbela—a virtual workplace that helps remote teams collaborate in immersive environments.

💡 The Future: More people will feel at home in digital spaces—not as an escape, but as a better fit for their personality and work style.

5️⃣ AI is Accelerating Breakthroughs in Health, Climate, and Science

Technology isn’t just about jobs and entertainment—it’s also solving humanity’s biggest challenges. During my keynote in Istanbul last year on Sustainability, I argued that;

“while humans have caused many of the sustainability problems we face, we must also be the ones to solve the same problems.”

This will come from diverse teams working creatively. By improving how we work, we significantly increase our chances of addressing these complex challenges.

• AI-powered drug discovery is cutting research time from decades to months.

• AI climate models predict extreme weather and optimize renewable energy.

• AI simulations are helping scientists understand complex biological systems, including aging.

🔹 Example: AlphaFold—AI that solved a 50-year-old biology challenge, predicting protein structures with unprecedented accuracy.

💡 The Future: AI won’t just make businesses more efficient—it will help solve existential human challenges.

6️⃣ We Now Have Unlimited, Free Access to Knowledge and Entertainment

In 1994, accessing knowledge meant buying books, CDs, and VHS tapes. Today, we have:

• Millions of free courses and lectures on YouTube.

• Unlimited music, movies, and podcasts at our fingertips.

• ChatGPT and AI tools that act as instant tutors for any subject.

🔹 Example: Khan Academy AI—AI tutors providing personalized education for students.

💡 The Future: AI-driven learning will make elite education available to anyone, anywhere, for free.

7️⃣ AI is Forcing Us to Ask: What Does It Mean to Be Human?

For the first time, we’re competing with machines that can write, design, and even simulate human emotions.

• Generative AI is blurring the lines between real and artificial creativity.

• AI-generated art is forcing new debates about originality and authorship.

• AI ethics is now one of the biggest conversations in society—who decides what AI should or shouldn’t do?

🔹 Example: Anthropic Claude AI—one of the leading AI companies developing ethical safeguards for AI alignment.

💡 The Future: As AI advances, humans will need to double down on what makes us unique—curiosity, relationships, and creativity.

Shape Your Own Future - Or Let AI Shape It For You

We can either let AI happen to us—or use AI to shape the future we want.

🚀 AI is not the enemy. Passivity is.

Sure, we can opt-out of unpalatable apps – an illusory protest vote that protects our conscience but changes little.

But, if we sit back and let big tech dictate how technology evolves, we lose.
Another approach, is to try to experiment, build, and adapt - to gain rather than lose.

Here’s what you can do today:
✅ Use AI as a partner, not a replacement. Try ChatGPT, AI design tools, or automation software.
✅ Find your niche. The best careers of the future will belong to those who understand how to work with AI—not against it.
✅ Think bigger. AI isn’t just about productivity. It’s about freedom, new possibilities, and creating the life you actually want.

Technology isn’t the problem.

The real danger is thinking we have no control.

The future belongs to those who engage, experiment, and build.

Will you be one of them?

A Day in the Life of a (Learning) Professional in 2035

I will be in Den Bosch, Netherlands delivering a keynote on April 1^st at the Next Learning Event. Come and say hello if you plan to attend, and if you haven’t booked a ticket, we have a special offer for subscribers.

Get Your 20% discount here

Optimistically,

Andy

Andy Spence is a Workforce Futurist, advisor, researcher, global keynote speaker, and agitator for a preferable future of work. He learnt to programme neural networks and philosophise about AI in his Masters in 1995. Although there was not much demand for these skills then, he worked for the big consultancies until he had saved enough to buy a round-the-world ticket in 2003. He started his own consultancy in 2006, and worked on over 40 digital transformation programmes. He now lives in Lisbon, learns Portuguese very slowly and tries to avoid the daily temptation of pastéis de nata.

Most of the talk around AI is on efficiency, productivity and skills. But what about organizational structures?

If you’ve been following Work3 for a while, you know we’ve talked about modern and innovative models like Holacracy (which revolutes around self-managing teams and no hierarchy) and Web3 / Blockchain based concepts.

The question for today is: will the new kid on the block (AI) also change how organizations structure and manage their workforce?

Let’s dive in!

Flattening Organizations

It’s impossible to have a perfect number, but management makes up an average of 10-20% of the total workforce. Unsurprisingly, cost is disproportionate, with high variations but likely starting at 30% of the total cost.

CEO-to-worker compensation comparison has skyrocketed from the 90s, and though it’s had its ups and downs, still remains at 300x. Even though this is for C-level, I would expect a similar curve (though not as steep) for middle management.

It seems like disparity is increasing further and further, and this has a huge problem hidden in plain sight: connection to reality. Many C-levels at big organizations, either because of generational differences or because of distance from operations, use a completely different map of the territory to navigate decision. Whilst there are instances where this is useful, it has a blind spot towards what really happens at the company and in the market, often resulting in long and costly crusades - anyone remember about Meta’s investment/gamble of just a few $bn in the Metaverse?

Another effect, is on the level of emotional and cultural distance. How can a CEO earning 300x more than one of her employees, think that they could have the same level of buy-in, or even worse, a sense of belonging?

So in these delicate times (likely to get even worse soon, I’m afraid) we see companies raking in billions in revenues slashing 5-15% in workforce, while increasing bonuses by 200% (again looking at you, Meta).

Others, are starting to cut middle-management (PwC, Unilever a while back) including Amazon, making waves with its plan to cut 14,000 managerial roles by early 2025, aiming to save up to $3.6 billion annually. CEO Andy Jassy wants to increase the ratio of individual contributors to managers by at least 15%, pushing the company toward a leaner, faster, and more agile structure. The strategy is framed as fostering a culture of urgency, ownership, and collaboration—essentially operating like "the world's largest startup."

Unsurprisingly, this restructuring has raised many concerns among employees about limited career advancement opportunities and growing dissatisfaction with Amazon's work environment. While Jassy highlights the benefits of flattening hierarchies and reducing bureaucracy, the move coincides with layoffs and a shift back to full-time office work, leaving many wondering whether the cost of efficiency is coming at the expense of employee morale.

Is all of this tied back to the rise of AI or is it result of macro-economic and market trends?

It’s impossible to say precisely, but if estimates (with their huuuuge ranges and doubts in terms of calculation methods) give anywhere a potential ‘efficiency’ improvement of 10-40%, I would expect CEOs looking at that sort of replacement / hiring avoidance. Of course this is incredibly simplistic and doesn’t take into account any kind of risks in timing, birth of potential new roles and so and so on - but that’s how we roll and what is being incentivized as an approach (just look at DOGE).

Also, by just having a look at this graph - I have a hunch that Amazon is trying to catch-up to the game, and trying to ‘reverse engineer’ how they improve their revenue/employee.

Anyway, this will deserve a piece on its own - as time will tell which factors plays the most, and what results come out of this wave of macho-capitalism (anyone coined this already? I’ll take credit if not).

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Hackman Authority Matrix

Hackman’s Authority Matrix (1986) provides a great lens to understand how workforce dynamics are evolving. It outlines a spectrum of team structures, from manager-led to self-managing, self-designing, and fully self-governing teams. Companies like Amazon are moving toward the latter, shifting authority from traditional management to teams themselves. While this concept isn’t new—Spotify and Stripe tried similar approaches years ago—they struggled to execute effectively.

AI enables these shifts and makes self-organizing teams more viable than ever. For example, departments like R&D might operate as self-governing teams, while Finance could function as self-managing units—all within the same organization. These structures can coexist, creating flexibility across business units.

It’s important to note that reducing the Management Index (MI)—the ratio of individual contributors to managers—isn’t the goal in itself. A lower MI only adds value when it aligns with improved processes and value generation. Flattening hierarchies without rethinking workflows or operations won’t yield meaningful results. This is where AI hopefully shines: it should empower teams with decision-support systems, reduces time to insight, and facilitates collaboration by cutting through complexity.

So, does this mean middle-management is doomed? Not really.

Human-AI Augmentation will impact individual contributors, but also management tasks. If less time is spent on admin, more strategic work should take its place, something that requires creativity - not efficiency (check out my recent piece on this and the distinction with productivity).

Human-AI Collaboration - A new type of management will arise: managing the input / output of the AI itself, plus the interaction between human workforce and the AI agent workfoce. Again, this deserves a deep dive on its own, but it helps us segway into the next section of today’s topic.

New Paradigms

So how will organizations look like?

There’s various options and none mutually exclusive, but one thing is for certain: adaptability, agility and innovation are going to be fundamental in the age of AI (and should be measured). With almost no barriers to entry to technology, and less capital needed to start ideas (or clone them) the pace of change will be higher. Arguably, big corporations with large balance sheets will still have an edge given their access to resources, but it’s not always for given - because of the ‘Innovation Dilemma’ (just look at Google for example, struggling to adjust to AI Search because they need to protect their ad revenues):

The innovator's dilemma describes how successful companies, focused on serving existing customers and improving current products (sustaining innovations), often overlook disruptive technologies that initially underperform. These disruptions eventually surpass established offerings, causing market leaders to fail because they prioritized short-term gains over long-term, potentially transformative innovations. Essentially, it's the paradox of doing everything right, yet still failing.

Here’s three options though we should be considering:

1. Exponential - We’ve already seen how the latest and hottest startups/scaleups are achieving staggering revenue results with smaller headcounts. Full article on this kind of org here.

2. Holocratic - Out with the pyramids, power will be spread through ‘circles’ of influence and role mobility to adjust Full article here.

3. Web3 Based - One of the first Work3 articles was about DAOs (Decentralized Autonomous Organizations), because in the wake of the Web3 ‘summer’ in 2020/2021, many people in the space took building companies to the blockchain, and found new models to organize compensation, capital, and collaboration. Full article here.

Beyond internal restructuring, we will also be reshaping the very fabric of operations. By streamlining interactions between people and processes, starting with IT and now expanding into areas like HR and finance, AI is becoming a critical decision support system. Furthermore, it's impacting external operations, automating and augmenting complex coordination in areas like supply chain and sourcing. As organizations adapt, they must not only navigate internal shifts but also leverage AI to optimize their broader operational ecosystems, ensuring they remain agile and competitive.

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We’ve seen how workforce, operations and processes are changing and how this can change organizational structures. The real question however, will be: who will be quick enough and brave enough to adapt?

My hunch is that larger corporations (see Amazon) may well go into ‘efficiency’ (or ‘Founder’) mode, but they may struggle to truly innovate, falling victim to the innovator's dilemma. In contrast, smaller, more agile organizations, embracing models like exponential growth, Holacracy, or DAOs, could leverage AI to achieve rapid phase transitions. They can more easily shift from a stable "franchise" state to one driven by "loonshot" innovations, fostering a dynamic equilibrium that balances efficiency with radical new ideas. The key will be understanding and managing these phase transitions – nurturing the "artists" alongside the "soldiers" – to unlock the full potential of AI and redefine the future of work.

Moreover, we're likely to see the rise of truly ‘AI Native’ organizations: companies built from the ground up with AI integrated into their core structure and operations. These organizations won't just adopt AI tools; they'll design their entire workflows and decision-making processes around AI, enabling maximum agility and innovation.

In this digital age, ‘designing work’ becomes key as we have many more choices from filling vacancies, to using contractors effectively, agencies, platform workers, AI agents etc.

We need to throw out the old Organisation Design rule book. (Spoiler: we plan to write much more about this in 2025). Who will design and build work, centralised teams like HR, or self-managed teams?

We have hypothesised about smaller organisations and a more liquid decentralised workforce with self-organised teams. It might be that AI that is the cover initially for layoffs, and then the catalyst for substantial new organisational shapes after.

Before jumping ahead too fast, some suggestions for work designers would be:

• Experiment - no need to go ‘all in’ at the start.

• Monitor progress - against intended goals.

• Speak to the people actually doing the work - feels silly to actually write this down, but you can write to me for examples!

• Measure the impact of AI on your organisation - There’s no one-size fits all, but there are some good tools and frameworks out there (more to come on this soon)

Many questions remain, but questioning is progress.

Ciao,
Matteo

What if we never needed to write a line of code in the future?

Are entry-level software engineers at risk?

Has the ‘Coding’ boom ended?

These are all questions I try to answer today, with a sprinkle of personal experience and examples from my personal and professional life with coding.

Let’s dive in!

WTF is ‘Vibe Coding’

Let’s start with the basics. What the hell is Vibe Coding?

Andrej Karpathy (founding team OpenAI, Tech Director Tesla and more) recently coined the term that describes how AI-powered apps are enabling coding without programming/technical skills. Forgetting for a second the very Californian name (though I admit it’s catchy) it marks a clear distinction between AI-coding assistants (think of Github Co-Pilot, and any IDE where engineers write code and get AI suggestions on code) and natively built apps that center around a chat interface.

So, anyone, without coding experience, can pretty much get into the game just by chatting with the app. Here’s an example of the starting screen by Lovable:

This is a true paradigm shift, well because not only it centers around using english as the coding language, but also provides an interface which lets you see in real-time changes you’re making to what you’re building.

Here’s an example from a very simple app that I asked it to build for ‘Layoff'-Lifeline’, a small set of resources and interactive tools I had been thinking to build for a while, but never had the time (or capability) to do so. This weekend, in just half-an hour, I got a functioning prototype going on, with a financial calculator, job search assistant that actually gets your CV and provides job opportunities and more.

It wasn’t all perfect, and I haven’t built a rocket ship - but the joy it brought me to see my idea come to life so quickly and effortlessly, definitely excited me.

The idea of ‘vibe’ is that given this experience, it becomes both easy for non-technical people to build their own projects, but also for slightly more technical folks to do changes quickly (i.e. ‘turn all of the buttons to green, make the icons larger and so on). It’s instant gratification for building. Better than social media, if you ask me.

So who are the main players in this up and coming space? Here’s a short list:

Cursor (cursor.so)

• Key Features: AI code generation, built on VS Code, user control, AI buttons for common tasks.

• Total Funding: $173.25 million.

• Stats: Over 360,000 paying developers and 40,000 paying customers. Used by engineers at OpenAI, Midjourney, Perplexity, and Shopify. Achieved $100 million ARR in 12 months .

Replit (replit.com)

• Key Features: Ghostwriter AI for code generation, browser-based IDE, cloud-based environment, user-friendly interface, extensive language support, real-time collaboration, built-in hosting.

• Total Funding: $272 million.

• Stats: Over 30 million software creators . Early focus on young developers and educational institutions .

Lovable (lovable.dev)

• Key Features: AI-powered full-stack app builder, natural language interface, rapid prototyping, integrates with GitHub and Supabase.

• Total Funding: $27.7 million.

• Stats: Over 140,000 registered users . Achieved $4 million ARR in four weeks and $17 million ARR in three months . Processes over 1,000 product builds daily .

Bolt.new (bolt.new)

• Key Features: AI-powered development with natural language commands, browser-based development environment, integrates with existing tools, rapid development.

• Total Funding: $105.5 million.

• Stats: Over 2 million user registrations, achieved $20 million ARR in two months . Claims to reduce development costs by 99% .

Rosebud AI (rosebud.ai)

• Key Features: Creates AI-powered characters and games, offers game development tools, simplifies game development with features like Sprite Sheet Generator and AI NPCs.

• Total Funding: $8.8 million.

Quick note about Cursor: it’s been the fastest growing app to reach 100M$, in just 2months.

It's Not All Sunshine and Roses

Now, before we all get carried away with the hype and start planning our early retirement from coding (just kidding!), it's important to remember that vibecoding, for all its magic, isn't without its limitations. Even the most advanced AI tools still have some kinks to work out.

• AI Can Be a Bit of a Diva: Like a temperamental artist, AI sometimes throws a tantrum and produces unexpected results. You might describe what you want perfectly, but the AI might misinterpret your instructions or generate code that doesn't quite hit the mark. This means you still need a basic understanding of what's going on under the hood to spot errors and guide the AI back on track.

• Complexity is Still a Challenge: While AI excels at automating simple tasks and generating basic code structures, it can struggle with more complex projects. Think intricate integrations, custom features, or anything that requires a deep understanding of software architecture. For those heavy-duty projects, you'll still need the expertise of a seasoned developer.

• The "Black Box" Problem: One of the challenges with AI is its lack of transparency. Sometimes it can feel like a "black box" where you input a prompt and get an output, but you don't really understand how the AI arrived at that solution. This can make it difficult to debug or modify the code, especially if you're not familiar with the underlying logic.

• The Creativity Conundrum: While AI can generate code, it can't (yet) replicate true human creativity and ingenuity. It can follow instructions and learn from existing data, but it can't come up with truly original ideas or solve problems in novel ways. So, if you're looking to build something truly groundbreaking and innovative, you'll still need that human spark.

The best software will likely be built through a collaboration between humans and AI, where humans provide the creativity, critical thinking, and problem-solving skills, while AI handles the grunt work and automates repetitive tasks. It's like a dynamic duo, each playing to their strengths.

How is this poised to change the Coding Job Market?

Fred recently posted a chart on Software Development Jobs in the US. We're seeing a 35% drop in software developer job listings compared to just five years ago. That's not just a little dip; it's a dive. And it's not just some US thing; it's happening globally, though Australia's oddly bucking the trend. Now, you might be thinking, "Okay, so what? Markets fluctuate." But this feels different. It's not just about the usual economic cycles.

Think about it: interest rates are up, VC funding is down, and suddenly, everyone's talking about how AI is going to make us all super-productive. Companies are probably thinking, "Why hire a bunch of new engineers when we can just get our existing team to do more with AI tools?" It's that whole "wait and see" approach, but it's having real-world consequences. And let's be real, after the hiring frenzy of 2021-2022, a lot of companies probably over-hired anyway. They're probably still working through that backlog - and adjusting for macro-economic context (looking pretty grim at the moment, yes).

We're also seeing these smaller teams, like at Linear and Bluesky, proving that you don't need a massive engineering department to build awesome stuff. So, it's a perfect storm: AI making coding easier, companies being cautious, and the realization that maybe, just maybe, smaller, more efficient teams are the way to go.

If 'Vibe Coding' takes off, maybe we'll see a surge in non-developers building software. And who's going to maintain and improve all that AI-generated code? Maybe that's where the next wave of developer jobs will come from. Or maybe, just maybe, we're looking at a fundamental shift in how software is created and who creates it.

Software for One?

One of the most exciting aspects of vibecoding is the potential to create "software for one" – highly personalized apps tailored to individual needs and desires. Think of it as bespoke software, handcrafted just for you.

This concept, highlighted by Kevin Roose in his New York Times article, opens up a whole new world of possibilities. Imagine:

• A fitness app that adapts to your unique body and goals. It analyzes your activity levels, sleep patterns, and dietary habits to create a truly personalized workout and nutrition plan.

• A news aggregator that filters out the noise and delivers only the stories that are relevant to you. It learns your interests and preferences over time, becoming your own personal news curator.

• A productivity tool that helps you manage your time and tasks in a way that works best for you. It understands your work style and helps you optimize your workflow.

These are just a few examples of how "software for one" could revolutionize the way we interact with technology. It's about moving away from one-size-fits-all solutions and embracing hyper-personalization.

This shift towards "software for one" has the potential to democratize software development like never before. It empowers individuals to become creators, not just consumers.

Imagine a world where anyone, regardless of their technical skills, can bring their software ideas to life. A world where a teacher can create a customized learning app for their students, a musician can build a unique sound editing tool, or a small business owner can develop a bespoke inventory management system.

This is the promise of vibecoding – to unlock the creative potential within each of us and enable us to build the software that we want and need, without limitations. It's about putting the power of creation back into the hands of the people.

It's a bit of a wild ride, and honestly, I'm just as curious as you are to see where it goes.

Ciao,
Matteo

By 2030, more than half of the young people entering the global workforce will be African. Africa boasts the youngest population in the world, with a median age of just 19.2 years compared to Europe’s median age of 44.7 years.

We are impressed by cute apps, but it’s the slow-moving sweep of demographics over twenty or thirty years that really drives our economies and societies.

600 million Chinese have joined the global workforce since the 1980s - the biggest poverty reduction in history. The consequences (unintended, or intended) was wage-suppression in the West, ongoing geopolitical power shifts, and the rise of populism.

For the first time in history there are more people on the planet over 65 than kids under 5. So as we all get older, who will do all the work that AI refuses to do?

This article considers the bull case for Africa which has a rapidly growing workforce hungry for opportunity. The rise of remote work and digital platforms means that a higher proportion of work can be done from anywhere.

Africa's Innovation Ecosystem: Beyond the Challenges

There are numerous books exploring Africa’s historical and contemporary challenges, including the lingering effects of colonialism, political instability, and inadequate infrastructure. Without ignoring these obstacles, I believe a compelling case can be made for Africa’s future, driven by technological advancements and a young, growing workforce.

Africa’s start-up ecosystem is thriving, with a record $6 billion raised in venture capital funding in 2022. Here are some of Africa’s fast growing Start-Up, Scale-Ups and Unicorns:

• FinTech Boom - Moniepoint - a payment processor handles over 800 million transactions a month, worth more than $17 billion. Flutterwave - this FinTech company operates in 34 African countries, has processed more than 400 million transactions, and served 1 million customers. Paystack - allows payments between 60,000 businesses and customers, powers 50% of Nigerian businesses and acquired by Stripe for $200 million.

• Mobility Revolution - Moove - the pan-African mobility fintech startup, closed a $100 million Series B funding round, Uber joined as an investor for the first time in Africa, further strengthening their partnership. Tugende - is a Ugandan startup that offers lease-to-own financing including for motorcycle taxis, enabling drivers to own their vehicles and improve their livelihoods. So far they have over 29,000 clients and created 800 jobs.

• HR and Workforce Solutions - Workpay - this Kenyan HR and payroll startup empowers businesses to pay their employees on time, and recently had a $5 million investment from Visa.

• e-Commerce - Jumia - has over 2300 employees and handling 22 millions orders a year.

• mWater - a startup in Rwanda allows 300,000 people to collect and analyse water quality data using their phones. This way, it will enable people to drink safe and clean water.

The Bull Case for African Growth

Since 1960, the average life expectancy has risen from 41 years to 64.

Since 1970, the proportion of young Africans attending university has risen nine-fold.

In recent years African authors have won the Booker prize, the Prix Goncourt and the Nobel prize for literature.

A positive case for African growth would include these factors :-

Education as Economic Catalyst: Bridging Traditional and Digital Learning

64% of Sub-Saharan African children complete primary school, compared to the global average of 87% (UNESCO). This gap highlights the need for sustained investment in education, particularly in rural areas, to ensure that Africa’s youth are equipped for the jobs of the future. While traditional educational infrastructure remains underdeveloped, digital tools like YouTube and online platforms are bridging the gap. With internet penetration rising from 25% in 2019 to 40% in 2023, countries like Ghana are investing in digital skills to prepare the next generation for a tech-driven future.

Digital Leapfrogging🐸

Traditional development economics might suggest there are certain steps that countries need to make to move up the economic value chain. But, looking at the past has its limitations due to different context and starting points. Africa is already leapfrogging traditional development stages, for example 70% of the population now owns mobile phones. So in financial services we see mobile banking before local branches.

Platforms like Eneza Education and Ubongo leverage mobile technology to deliver quality learning resources to rural and underserved areas. These innovations not only improve literacy and numeracy but also prepare students for tech-driven industries.

Urbanisation🏙️

By 2050, Africa's urban population is expected to double, adding over 800 million people to cities like Luanda, Dar es Salaam, Cairo, Kinshasa, Lagos, and Johannesburg. These cities are becoming hubs of innovation, entrepreneurship, and employment, attracting both local and international investments.

Generation Hustle

With 341 million working-age individuals in Sub-Saharan Africa—and 12 million more entering the workforce each year—many young people are turning to entrepreneurship. Known as 'Generation Hustle,' they juggle multiple side businesses to make ends meet, driven by ambition and creativity.

The Decentralised Workforce in Africa

Internet penetration has grown from 25% in 2019 to 40% in 2023, driven by robust investments in broadband, mobile technologies, and undersea cable systems.

Our article on the Decentralised Workforce highlighted how millions of people are now making a living from digital infrastructure and this applies to Africa too.

Here are some of the work-matching platforms operating mainly in Africa.

• Ajira Digital in Kenya connects people to online freelance opportunities. A program supported by the government has enabled over 1 million young Kenyans to earn supplemental income by providing services such as content writing, virtual assistance, and graphic design.

• There are plenty of job-boards like Jobberman and Brighter Monday with 4.6 million jobs seekers and 160,000 employers using their platforms.

• Gozem - launched in Togo, providing ride-hail services, with 1 million users.

• Talenteum with 20,000 professionals and Tunga focused on scaling software teams are connecting African professionals with international clients. Andela has 150,000 highly skilled tech professionals on their roster and Decagon train thousands of young Africans and connect them with high-paying jobs in Europe, North America, and beyond.

Also some of the Global players have a presence in Africa too e.g. Upwork, Fiverr, Toptal, Task Rabbit.

So the African workforce is constantly adapting to global demand for skills, enabled by a new generation of global work-matching platforms.

Forging Global Partnerships: Africa’s Role in a Multipolar World

Today, Africa is home to 15% of global population, accounts for just 3% of global output and provides 5% of growth. If sub-Saharan Africa can repeat Asia’s transformation, it “will become the next major engine of global growth”, argues an investment research note by Bridgewater.

As Africa’s demographic and economic influence grows, the continent is increasingly positioned as a key player in the global economy. By 2050, Africa’s working-age population is expected to reach 1.3 billion, accounting for a significant share of the global labor force. This growth presents not only opportunities for Africa but also the potential for transformative partnerships with regions like Europe. By leveraging complementary strengths—such as Africa’s youthful workforce and Europe’s technological and financial resources—these regions can collaborate to address shared challenges, from labor shortages to climate change, and drive sustainable growth.

While the European Union has made strides in political and economic coordination, Africa is forging its own path through initiatives like the African Continental Free Trade Area (AfCFTA). This aims to unite 54 countries into the world’s largest free trade area with 1.47 billion people. This initiative is projected to lift 50 million people out of extreme poverty.

The EU has significantly boosted its financial support for African infrastructure projects, with contributions reaching €972 million in co-financed operations with the African Development Bank over the past two years. This includes partnership to bridge the digital divide in Africa and significant funds to support Africa's green energy initiatives. The EU and the African Union launched the Africa-EU Space Partnership Programme. China’s Gotion High Tech announced a $1.3 billion investment to establish Africa’s first EV battery gigafactory in Morocco.

Africa’s demographic dividend, combined with its rapid technological adoption and entrepreneurial spirit can position the continent as a key driver of global growth in the 21st century.

Looking south,

Andy

Loneliness isn’t just a personal struggle—it’s a workplace epidemic.

Despite our hyper-connected digital world, more workers than ever feel isolated, disconnected, and disengaged.

This isn’t just a human problem; it’s a business problem.

The U.S. Surgeon General has equated loneliness to smoking 15 cigarettes a day in terms of health risks. It erodes mental well-being, damages productivity, and even accelerates employee turnover.

Yet, while loneliness is widely discussed, the workplace’s role in shaping and mitigating it is often overlooked.

• Does being alone cause loneliness?

• Is the rise of remote work to blame?

• Are younger workers now lonelier than previous generations?

• And most critically, what can employers do to rebuild connection in an increasingly fragmented work landscape?

I Think We’re Alone Now?

Putnam’s book outlined an erosion of social capital and community engagement towards the end of the last century. Fast forward 25 years and we can see more eating alone, drinking alone, sleeping alone, shopping alone, relaxing alone, even worshipping alone.

Self-imposed solitude might just be the most important social fact of the 21st Century in America according to Derek Thompson in his essay The Anti-Social Century.

Loneliness is Rising—Especially for Younger Workers

• Young people are feeling it the most: In the UK, it was once the over-80s who reported the most loneliness, but by 2023, under-30s have become the most affected group. About 6% of young adults say they meet friends, relatives, or colleagues less than once a month.

• Americans are spending more time alone than ever: The typical American now spends more time alone than at any point since 1965.

• Declining social interaction: The number of adults who have dinner or drinks with friends on any given night has dropped by over 30% in the last two decades.

• Romantic relationships are declining too: The number of under-35s not living with a partner has nearly doubled since 1980.

Loneliness Vs Aloneness

Loneliness is not the same as being alone.

There is not strong evidence that being alone necessarily leads to reported loneliness. In fact some researchers talk about self-imposed solitude as a healthy emotional response and a cue to getting off the sofa to meet people.

Loneliness is not a binary state, and feelings of loneliness are heavily shaped by cultural norms. This subjectivity presents a fundamental measurement challenge. There is no empirical support for the fact that loneliness is increasing, let alone spreading at epidemic rates.

So as the social contract appears to crumble in the 21st Century, we will struggle to isolate the reasons why. The decline of social capital, as explored in Robert Putnam’s Bowling Alone, has reshaped our relationship with work. Once, jobs were not just a source of income but a foundation for community. Today, as more people work remotely or in short-term contract roles, opportunities for organic social interaction have shrunk.

Why Loneliness At Work Matters

One in five employees worldwide currently feels lonely at work, according to the 2024 State of the Global Workplace report by Gallup.

Loneliness is more than a personal inconvenience—it has measurable health and business costs. Studies from The Lancet and Holt-Lunstad et al. show that chronic loneliness increases the risk of early mortality by 26%, equivalent to the impact of obesity or excessive smoking. Socially isolated employees experience higher stress levels, poor sleep, and weakened immune responses.

For businesses, the impact is just as stark. Lonely employees report:

• Lower job satisfaction and engagement.

• Reduced productivity—lonely workers are 3.8x more likely to have unproductive workdays.

• Higher turnover—disconnected employees are twice as likely to leave their jobs.

But before we send a whatsapp invite for team drinks tonight, it is worth considering some myths about loneliness - the first is that loneliness can be solved with in-person work.

Debunking the Remote Work Myth

It’s easy to blame remote work for rising workplace loneliness—but the truth is more complex. Research by Constance Noonan Hadley and Sarah L. Wright shows that loneliness is not about where you work, but the quality of workplace interactions.

People can feel lonely even in a crowded office. A survey from workforce analytics firm Worklytics found that coming into the office once a week provides just as much connection as coming in four or five times. Simply being physically present doesn’t solve the problem—what matters is how workplaces structure social interactions.

What Can Employers Do To Reduce Loneliness At Work?

A return-to-office mandate won’t automatically fix workplace loneliness—but employers can take steps to design work environments that foster meaningful human connection.

Here are five key strategies:

1️⃣ Measure and Diagnose the Problem

• Loneliness should be tracked just like engagement and productivity.

• Use pulse surveys and Organizational Network Analysis (ONA) to map social fragmentation.

• Test interventions—does introducing shared lunches actually improve social bonds? Are remote workers experiencing more loneliness than in-office employees?

2️⃣ Redesign Workplaces for Connection

• Physical offices should prioritise communal spaces—shared meals, informal gathering spots, and structured peer networking.

• Bring back canteen culture and casual social hours to create micro-communities.

• Social events should be low-pressure and inclusive—not just pub drinks or awkward icebreakers.

3️⃣ Use Technology Wisely

• The internet has contributed to social fragmentation, but it can also be part of the solution.

• AI-powered apps like Woebot Health offer mental health support, and workplace communities can rebuild some social capital..

• Employers should invest in better-designed online spaces where employees build real connections, not just work threads.

4️⃣ Work Has Always Been Social

• A quarter of all couples used to meet at work—but with fewer in-person interactions, younger workers may be missing out on these organic relationships.

• As the romantic Scott Galloway argues, remote work is a disaster for young professionals trying to form social bonds.

• While companies don’t have a matchmaking 😘 role, they can create environments where people feel comfortable socialising, collaborating, and forming friendships.

5️⃣ Train Managers to Lead with Empathy

• Employees feel lonelier when they lack psychological safety—the ability to speak up, ask for help, or admit struggles.

• Train managers in empathy and active listening—Ogilvy’s Mindful Manager program with Calm is one model for improving workplace well-being.

The Business Case for Belonging

The biggest competition for employers isn’t always a rival company—for an individual it’s the allure of digital entertainment, the creator economy, and alternative income streams and lifestyles.

Why work in an office when Twitch, Discord, Netflix and YouTube offer endless entertainment while enjoying mum’s cooking?

Winning employers will need to sell belonging, purpose, and community not just salaries. Those that succeed will attract the best talent and create workplaces that people actually want to be part of.

Final Thoughts: The Future of Work is Social

Work has never just been about productivity—it has been a central force in shaping human connection and community.

Employers have a once-in-a-generation opportunity to redesign work for belonging. Those that do will boost engagement, loyalty, and even reduce some loneliness.

How is your company tackling workplace loneliness? I’d love to hear what’s working—and what’s not.

Nurturing connections,
Andy

Workforce Futurist

We are living in a world where technology is a double edged sword.

On one side, it democratizes access to information, education, connection and increases competition.

On the other side, it pushes businesses and individuals even further in a ‘performance’ and growth at all costs context.

The future of work in the age of AI will be extremely polarized: offering both unparalleled opportunities and exacerbating existing inequalities.

The rise of disparity

Let’s start with the famous ‘Elephant’ chart of economic growth (also featured in Andy’s great article ‘Five Graphs that Challenge the Way we Work’).

Why an elephant, you say? It has a hump that includes the world’s poorest, mostly made up of people from developing countries; a valley that contains the working and middle class of the developed world, but also the upper class of poorer countries; and a “trunk” made up of the global elite.

The hump and subsequent valley on the left illustrate the enormous growth emerging countries saw relative to the middle and working classes in developed countries. This difference in growth has driven global inequality down to levels not seen since the 1700s.

But the new estimates also revealed that the richest 1 percent saw more growth than any other income level—resulting in the elongated trunk on the right of the chart.

How the elephant’s trunk will move is the key element we need to watch out for, especially in the light of AI.

Productivity vs Compensation

When we think of AI, we think of productivity. But who benefits from productivity gains?

The answer can be found in this graph, showing how hourly compensation has been closely falling the productivity growth up until 1980, and then started to decouple. So supposedly, if that productivity actually created wealth/value, it ended up in the 1%.

Now there’s several ways to look at this data. ‘The wages have grown!’ some will say. True, but cost of living has risen dramatically too. This means, that adjusted to inflation, the widening gap has not played out in favor of workers, but more to businesses.

Just think of how technology impacted internet economics; more than 60% of ad spend outside of China goes to Amazon, Meta and Alphabet. If AI is dominated by the same companies (plus OpenAI), we’re still going to be putting the majority of gains in the hands of the few.

What about productivity at work, you say?

In this transition, being more productive with AI does not imply growth of compensation. From my point of view, the only way that this could really work, is if organizations turned to a more ‘Web3-like’ distribution of profits, or if work was paid by outcome.

Right now, all businesses operate and compensate based on time. Time-based pay was straightforward to calculate and administer during the industrial revolution, making it suitable for businesses that needed to employ staff for general roles where productivity was hard to measure.

Time-based salaries are going to be out of date for several reasons:

1. Lack of Incentive for Productivity: Time-based pay does not encourage greater productivity since employees are paid regardless of their output level. This can lead to inefficiencies if some workers produce more value than others without additional compensation.

2. Modern Work Environments: With advancements in technology and changes in work environments (e.g., remote work), measuring productivity by time alone becomes less relevant. Output-based salaries can better align with modern performance metrics that focus on results rather than hours worked.

3. Global Competition and Innovation: In today's competitive global economy, companies often seek ways to motivate employees beyond just paying them by the hour or month. Performance bonuses or output-based incentives can drive innovation and efficiency more effectively than traditional time-based models.

What happens with an ‘output’ based compensation?

Let’s say we’re living in 2030 (or 2035) when AI has already automated most of the white-collar tasks it could take. We can imagine one scenario where history simply repeats itself (new jobs are created, but we still employ and compensate in the same way as today) or an alternative scenario, where 50% of the compensation is time-based and 50% is outcome based.

This could be broken in three main components:

1. Outcome-Based Metrics:

   • Project Completion Rates: Focus on how efficiently tasks are completed rather than the time taken

   • Milestone Achievements: Track progress by identifying key milestones within projects

   • Quality of Deliverables: Assess the quality and punctuality of project outcomes

2. Feedback and Peer Assessment:

   1. Use 360-degree feedback to evaluate productivity based on peer comments and interactions.

3. Token Economy - Many Web3 companies use tokens (like digital ownership shares) as part of their compensation packages. This allows employees to benefit directly from the project's growth, aligning their interests with the company's success (more on this in ‘The Decentralised Workforce’ and ‘Work3: The Internet of Careers’)

Clearly, there’s also downsides to this solution like any, but any company that would bake these elements into their operating and people model could harness the power of smaller teams, but with higher level of skin in the game and distributed / fairer incentive.

Last, but not least, I imagine a future where:

Freelancers are the majority of workers - Statista (chart above) estimates that in 2028 just in the US they will be 90 million, 51% of the total workforce.

Solopreneurship will be taught in schools and half of workers will have tried to launch their own business.

Tools and Businesses will be paid for results - The more digital services and skills become commoditized, the more SaaS tools and service businesses will need to use AI productivity to drive new business models and compete for clients.

Case studies: Outcome-based pricing in action

Let’s examine some real-world examples of companies successfully implementing outcome-based pricing models.

Riskified, an ecommerce fraud prevention provider, uses advanced algorithms to assess transaction risks in real time, providing instant approve/decline decisions. Riskified guarantees approved transactions against fraud and charges only for successfully approved, fraud-free transactions. This model incentivizes Riskified to continually improve its algorithms and deliver value, as the company only profits when its solution effectively prevents fraud and increases sales.

Riskified’s Chargeback Guarantee is prominently featured on its website

Source: Riskified

Riskified’s approach is particularly effective because it directly reduces client expenses. This makes outcome-based pricing more appealing, as customers can easily quantify savings and see a link between the service provided and its financial impact.

iDenfy, another player in the fraud management space, offers KYC (Know Your Customer) verification services with a similar outcome-based pricing model. It helps businesses verify customer identities and prevent fraud, charging only for successfully approved users. Both Riskified and iDenfy showcase how fraud prevention and identity verification services are well suited for outcome-based pricing, as success is clearly defined and measurable, directly linking service to value.

Shifting from fraud prevention to customer service, Intercom’s AI chatbot, Fin, offers another innovative example of outcome-based pricing. Launched in 2023 and closely watched as an early AI-based implementation, Fin costs $0.99 per successful resolution. This model, where clients pay only for effective AI interactions, demonstrates how companies can blend traditional usage-based and outcome-based pricing.

Intercom’s July 2024 pricing for its AI agent, Fin

Source: Intercom

As an early adopter in AI services, Intercom and its approach may accelerate a broader trend. AI’s ability to process vast data and deliver measurable results makes it particularly suited to outcome-based pricing, potentially reshaping pricing strategies across various SaaS sectors.

Productivity vs Human Connection

One last consideration about time and work. Until we have people that compare humans with machines, and think that human productivity is a simple multiplier of time, we’re doomed as a society. Think of it: the man behind the mission of AGI and automation of everything, relies on 120 hour work weeks from humans. There is scientific evidence suggesting that beyond a certain threshold (typically around 40-50 hours per week), additional hours worked do not necessarily translate into increased output but rather lead to decreased efficiency per hour due to fatigue. There is also scientific evidence that multitasking can decrease productivity by up to 40% due to the time spent switching between tasks. This switching process disrupts information processing, leading to decreased memory and higher error rates.

All of this can lead to mental fatigue and even negatively affect decision-making skills by reducing focus and creativity: exactly the two vital skills we will need to thrive in the future.

Until we get that right, in culture, in media, in organizational models, we will just be hamsters running around a wheel, with a tip of machoism and populist rhetoric masking our stagnation.

Successful societies can respond to this by a) adapting b) putting humans at the center c) thinking of equitable access. That’s why lifelong learning, support for entrepreneurialism and new mechanisms for financial securities need to be designed at the core of culture, and public support - not just at the private level (all of these widely covered in our articles ‘Reinventing Work for the Digital Golden Age’ and ‘Building a Better World Without Jobs’.

Last order of the day, for fun purposes only (so feel free to stop here if you’re not in for the treat) - the response from Gemini on how the Elephant shaped trunk could change with the impact of AI:

• The Dromedary: A single-humped camel with a massive hump representing the ultra-rich, while the rest of its body is scrawny and malnourished, symbolizing the struggling masses.

• The Platypus: A bizarre creature with a duckbill, beaver tail, and otter feet, representing the confusing and unpredictable nature of the AI-driven economy, where traditional job categories blur and new, strange ones emerge.

• The Bloated Tick: A grotesque creature swollen with wealth, sucking the lifeblood out of the rest of the economy, symbolizing the parasitic nature of extreme wealth concentration in the age of AI.

• The Three-Headed Cerberus: A mythical beast guarding the gates of wealth, with each head representing a different sector of the AI-powered elite: tech giants, financial wizards, and political puppeteers.

• The Invisible Man: A ghostly figure representing the vast majority of the population, rendered invisible by the overwhelming power of AI and the elite, struggling to survive in a world where their skills and labor are no longer valued.

Tried to ask it for a sketch, but this is all I got.

Ciao,
Matteo

Would an AI Operating System really do most of our mundane work and be better than us at innovation and collaboration?

Last week, as OpenAI announced ‘Operator’, its first attempt at an agent, I’m sure I wasn’t the only one thinking that we’re getting closer to AI taking over even more tasks. There’s already some great examples out there, and even though right now it’s only available for those paying the 200$/month ChatGPT plan, it will no doubt become more accessible soon. The era of your own ‘digital workforce’ will empower many projects. At an individual level, you could be running several jobs at the same time (some have tested it to play Poker, or compile surveys to earn some extra cash). At the company level, the holy grail of efficiency will be up for grabs.

But today I wanted to focus on the longer-term picture. I think AI is going to become an Operating System (i.e. Windows). It’s going to happen first on a technical level (i.e. running your computer) and then, at a more strategic/business level (i.e. running your business).

Here’s how I think it will work.

Stage 1: Taking Over our Computers (less than a year)

This stage will redefine how we interact with our computers.

• Beyond Apps (and APIs): AI could fundamentally change the way we interact with our computers, moving beyond the traditional app-centric model to a more conversational and task-oriented approach. Think less "opening a spreadsheet program" and more "analyze this data and create a summary."

• Personalized Workflows: AI could personalize our workflows, anticipating our needs, automating repetitive tasks, and suggesting optimal tools and resources based on context.

• Contextual Awareness: AI could understand the context of our work, seamlessly integrating information from different sources and applications to provide a more holistic and intelligent computing experience.

Current functionalities are slow, and imperfect - but if we assume a similar pace of improvement to the one we experience in the last year, I don’t see why this kind of feature couldn’t be perfected in this time frame.

Of course the first step is for this to be an app, an external provider. But how long do you think it will be before it’s integrated (like Microsoft’s CoPilot) inside the hardware / computers out of the box?

To begin with, this will be the kind of features that try to revive consumerism and interest in the mobile phone industry (we can already see this happening), the shiny object. Just have a look at how smartphone and PC sales are slowing down:

In terms of PCs, we could hypothesize two scenarios:

1 - Microsoft cracks the secret sauce it and keeps its power

2 - Someone else (OpenAI? a complete outsider?) potentially with an Open Source version launches their own product

Mobile is more tricky: on one side, there’s the rise and fall of wearable AI (Humane Pin, Rabbit R1) that may have limited market potential for the B2C market (although it is currently sitting at 4bn$ projected to be at 40bn in 2030). On the other side, we there’s smart eyewear, where Meta’s dream is to substitute mobile phones alltogether at a certain stage.

That said, the real game for AI is for businesses, not end customers. Apple Intelligence has been a flop, and yes it’s very cool to have a personal assistant that books my restaurants and flights, but the gain both in terms of time and monetary value is limited.

That’s where Operators (or Agents) are supposed come in, doing most of the sourcing for recruitment, prospecting for sales, scheduling, and even running sales and customer care calls. This isn't just about automating simple tasks; it's about AI agents becoming deeply integrated into business workflows, acting as a sort of digital workforce, which we discussed in this article a few months back.

At a hardware level, we’re also going to see operating systems that learn what tasks a user prioritizes and automatically adjusts computer resources like processing power and memory.

A likely scenario involves AI controlling software through simulated inputs and visual recognition, enabling faster, more efficient multitasking. AI with feedback capabilities could even monitor system health, performing updates and repairs.

The operating system itself could be transformed. Instead of desktops and windows, AI could generate interfaces on demand, adapting to specific needs. Content would be dynamically generated or retrieved. Interaction could also evolve, moving beyond traditional input methods to include conversational language, sign language, and even subtle body movements. Imagine simply glancing at something and gesturing vaguely, with the AI understanding your intent. This intuitive interaction promises a future where computers truly anticipate our needs.

But this potential shift has a big downside. The very speed that makes AI so attractive also means that mistakes can escalate quickly. While offering the potential for efficiency gains, it also introduces the possibility of rapidly accumulating errors. More importantly, if we’re looking specifically at business operations, APIs and integrations allow automations at a deeper level which are more efficient and less costly.

Stage 2: Taking Over our Businesses (in five to ten years)

This stage is being promised as the holy grail of efficiency and returns. PwC estimates that AI could contribute up to $15.7 trillion to the global economy in 2030¹, more than the current output of China and India combined. Of this, $6.6 trillion is supposed to come from increased productivity and $9.1 trillion is likely to come from benefits to consumers.

An AI Operating System could really be the dream come true of the ‘business in a box’ approach that morphs according to the specific context and use case. It could be the way that consulting, or IT companies providing services pivot their business models and thrive in a future where access to technology, software engineering and most of the technical skills we use today are a commodity.

A simple analogy is the one with the internet. Even though it’s been around almost thirty years, it took almost two decades before ‘Internet Native’ companies started to come around. Same thing with mobile.

An AI Operating System for businesses will touch both the technical side of things (integrated into hardware, as we discussed above) but also into the execution.

Some things it could do:

• Automated Workflow Management: Dynamically allocate resources, optimize task sequencing, and predict potential bottlenecks across all departments. Think of it as a real-time air traffic controller for the entire business.

• Personalized Customer Experiences: Analyze customer data to anticipate needs, tailor product recommendations, and provide proactive support, creating individualized journeys that maximize engagement and loyalty.

• Predictive Analytics & Forecasting: Go beyond historical data to predict future market trends, customer behavior, and operational needs, enabling proactive decision-making and minimizing risk.

• Intelligent Supply Chain Optimization: Manage inventory levels in real-time, predict supply disruptions, and dynamically adjust logistics to minimize costs and maximize efficiency.

To be AI Native, all of these components will need to be AI Native.

As business models change, people will acquire new skills with the power of AI solutions, and technologies will accelerate faster than ever before due to the compounding powers of these foundational models. Revenue generation will also accelerate by 50% according to Bessemer Ventures.

Canva achieved it in three years, Zoom in 3.5 years, and Twilio in four years. Emergent leaders such as OpenAI and Anthropic are well on their way to reaching not only Centaur status ($100M ARR), but becoming some of the fastest growing technology businesses in recent history. It’s estimated that AI-native companies will reach $1 billion ARR 50% faster than their cloud counterparts.

This type of AI Operating System will require a different type of organization: the Exponential Organization.

Organizations will transition from traditional top-down structures to decentralized ExO-style models, leading to significant changes in autonomy. Key transformations include increased Human-AI collaborations, the adoption of DAOs by nonprofits and the public sector, and participatory budgeting for decentralized decision-making. These organizations will rely heavily on technologies like blockchain and AI. The rise of DAOs will also see a shift from temporary staff to member-based roles. Moreover, employees will enjoy more customizable career paths and continuous learning opportunities. Emphasis will be on nurturing a positive organizational culture that values autonomy and innovation. Ethical considerations, including equitable decision-making and data privacy, will become paramount.

What are the implications for the Future of Work?

One thing is for sure: we are in for a complex landscape with several key implications:

1. The Rise of SuperOperators
   Rather than completely replacing human workers, we're likely to see the emergence of "SuperOperators" - highly skilled professionals who leverage advanced AI capabilities. These individuals will act as orchestrators, working symbiotically with AI systems to achieve unprecedented levels of productivity. They'll need to master the art of prompt engineering, system integration, and strategic oversight, becoming the bridge between AI capabilities and business objectives.

2. The Transformation of Traditional Employment
   As Ronald Coase's theory suggests, with transaction costs approaching zero through AI-driven efficiency, we're likely to see a fundamental restructuring of traditional organizations. This could lead to:

• Smaller, more agile core teams

• Increased reliance on project-based work

• The rise of AI-human hybrid teams

• A shift from traditional employment to more flexible, outcome-based arrangements

3. The Evolution of Human Value
   Rather than leading to universal leisure, AI Operating Systems will likely create a new paradigm where human value lies in:

• Creative problem-solving

• Strategic decision-making

• Emotional intelligence and relationship building

• AI system oversight and ethical governance

• Innovation in human-AI collaboration models

4. Economic and Social Implications
   To address the potential wealth concentration that AI automation might create, several adaptations will be necessary:

• Implementation of lifelong learning programs to help workers adapt

• Development of new economic models that better distribute AI-generated wealth

• Creation of improved work-matching infrastructure to connect people with opportunities

• Establishment of universal basic services or income systems to support transition periods

5. The New Entrepreneurial Landscape
   The democratization of AI capabilities through Operating Systems could lead to:

• Lower barriers to entry for new businesses

• Increased opportunities for micro-entrepreneurship

• New forms of value creation through AI-human collaboration

• The emergence of new industries focused on AI augmentation and oversight

6. Policy and Societal Adaptations
   To ensure an inclusive future, several key areas need attention:

• Education reform to emphasize AI literacy and human-centric skills

• Regulatory frameworks for AI deployment and usage

• Social safety nets for displaced workers

• Investment in infrastructure for the new digital economy

It won't be a simple matter of AI replacement or human obsolescence.

Instead, it will likely be a new ecosystem where the most successful individuals and organizations will be those who can effectively combine human creativity, judgment, and emotional intelligence with AI's computational power and efficiency.

The goal should be to use AI Operating Systems not just for efficiency, but as tools for human empowerment and societal advancement (which we covered in What comes next for Wealth, Work, and Leisure?)

As always, let us know what you think in the comments!

Ciao,
Matteo

Many workers seek greater autonomy, flexibility, and equity in their professional lives. For many, even achieving two of these would represent meaningful progress.

As traditional jobs fragment into discrete tasks, digital platforms are stepping in to rebundle these tasks into opportunities for freelancers and gig workers, creating new pathways for income generation.

The digital workforce has evolved far beyond ride-sharing and food delivery.

There are examples of teachers using platforms for private tuition, livestreamers making millions from YouTube, and kitchen-table solopreneurs.

This week’s Workforce Futurist article looks at how digital platforms are helping people make money and reach their potential outside regular employment.

Why work off a Platform?

Several factors drive individuals to pursue gig work or develop side hustles, these include:

Neither working, nor looking - under 30s are rejecting traditional employment. Platform work often fills the gap left by disinterest in traditional employment. The level of NEETs – not in Education or Employment, is 15% in UK. Two-thirds of these are ‘neither working, nor looking’. Compared to earlier cohorts of society, A J.O.B is not required for this group to access decent quality streaming entertainment 24 hours a day, to find a date, and generally live in societies where they won’t starve. Platform work has a low-barrier to entry to top-up their cash.

No way never - Gen Z is increasingly selective about employers, prioritising ethical alignment and flexibility over traditional full-time roles. 44% of Generation Z have turned down an employer based on personal ethics or beliefs. (Source – Deloitte Survey) with another survey reporting that 75% of Generation Z have no plans to go full-time in their whole lives. (Source – Dazed magazine survey)

Increased flexibility - for those reading this article and thinking about what to order off the food delivery app tonight, working from a platform might seem like the last resort. There is no sick-pay and no pension. However surveys of platform workers show a different picture - London Uber drivers reported a strong preference 80% for flexibility over labour rights. (Source - Berger et al. 2019)

One-Person Digital Empires – it’s not all about putting enough food on the table, many entrepreneurial individuals have used platforms to earn substantial incomes. It’s hard to cut through the hyperbole, but there are high-profile examples like NomadList and Kat Norton’s Miss Excel.

People > brands – society has an underlying loneliness trend which explains many phenomena in business and politics. In an anodyne AI-generated world, people want to engage with real people, not a hammy consumer and employer brand. This is a recommended essay on the topic.

How many people are their own Boss?

Traditional gig economy roles include freelance, contract, and project-based work. Platform work, however, involves using digital platforms to trade labour, goods, and services.

It is hard to estimate the extent of the gig economy globally, but here are some factoids:

• The size of the gig economy is estimated at $556.7 billion in 2024 and is projected to more than triple to $1,847 billion by 2032 (Source - WEF).

• 16% of U.S. adults earned money through an online gig platform in 2021 (Source -PEW Research Center).

• 14.5% of EU workers are self-employed, and 48% of workers globally are self-employed and work informally (Source – OECD)

Direct Service Marketplaces: From Side Hustlers to Specialists

The gig economy has evolved from simple side hustles to specialised marketplaces that empower caregivers, developers, and other skilled workers.

While Uber and DoorDash dominate headlines, specialised marketplaces are growing rapidly. Upwork reports that 59 million Americans performed freelance work in 2023, contributing $1.35 trillion to the US economy. As you can see from the main image, Upwork report that there has been a significant uptick in interest related to artificial intelligence (AI) and machine learning (ML) professionals.

High-skill platforms like Toptal, which accepts only the top 3% of freelance talent, report average developer earnings of $70-120 per hour, challenging traditional consulting models.

And these platforms also offer opportunities for those with all sorts of skills, from Carers to Dog Walkers, and Songwriters to Strippers. See more at the Sidehustle stack.

The pandemic accelerated this trend, with platforms like TaskRabbit seeing a 50% increase in new tasker registration. Care.com reports that professional caregivers can earn 25% more through their platform compared to traditional agencies.

In Kenya, platforms like Ajira Digital connect youth to online freelance opportunities. A program supported by the government has enabled over 1 million young Kenyans to earn supplemental income by providing services such as content writing, virtual assistance, and graphic design.

The Creator Economy: Building One-Person Digital Empires

The creator economy offers unprecedented opportunities for individuals to monetise their expertise and passions in education, entertainment, and beyond.

The creator economy has matured into a sophisticated ecosystem.

It is estimated that more than 50 newsletters on Substack earn over $500,000 annually, with established journalists moving away from being employed by a paper to running their own business.

Gaming platform Twitch reports that over 50,000 Partners earn full-time incomes through streaming, with top creators generating millions through multiple revenue streams. Former classroom teachers are now earning six figures through Outschool, transforming kitchen tables into classrooms for kids worldwide. Teachable reports that over 500 course creators earned more than $100,000 in 2023, with the platform facilitating over $1 billion in creator earnings. Outschool's teachers, earn an average of $50 per hour teaching specialised online classes.

As of 2024, the global creator economy was valued at approximately $250 billion, with projections estimating it could reach $528 billion by 2030.

The creator economy offers not just income, but autonomy – a stage where millions build audiences and income streams on their own terms.

Digital Commerce: From Kitchen Tables to Global Marketplaces

Digital commerce platforms have democratised entrepreneurship.

Shopify reports that its merchants generated $444 billion in economic activity in 2023.

The platform hosts over 1.7 million merchants, with the average successful store generating $72,000 annually. The Shopify Global Entrepreneurship Index illustrates the economic impact in their ecosystem across 40 countries.

Etsy's success stories are equally impressive. The platform's active sellers grew to 7.5 million in 2023, with top sellers earning over $100,000 annually. What's notable is the rise of hybrid models – 48% of Etsy sellers combine platform sales with other income sources.

There are also other ways to earn, including:

Passive Income Innovation - Turning Time Into Tokens - Passive income is no longer just about dividends or rent – it’s about earning while you scroll, game, or code. The Brave browser, with over 67 million monthly active users, pays users in BAT tokens for viewing ads.

The Decentralised Workforce - Challenges and Opportunities

For all the talk of millionaires, kitchen-table entrepreneurs and side-hustlers, there are problems with the platforms. Platform fees often range from 10-30%, eating into worker earnings. Benefits access remains a critical issue – only 22% of full-time platform workers report having health insurance through their work. To address these challenges, policymakers must rethink social protections to ensure platform workers have access to fair earnings and essential benefits.

As I have written and spoken about previously, the Future of Work is not all about JOBS. We need a new social contract that provides financial security, innovation and problem-solving, and meaning. Policy-makers in successful cities and countries need to be developing long-term solutions in these areas, to include all types of workers.

In a global workforce of 3.5 billion people, these platforms are becoming lifelines for millions in emerging markets, enabling workers to supplement their incomes. Workers with a laptop and an internet connection can tap into global demand, regardless of their location.

Work is not going away, our most important work is building healthy and harmonious societies.

Some Questions for Workforce Futurists

In the world of work, old centralised command-and-control modes and business models will fall apart as better structures take their place.

This generates some interesting questions for workologists…

1. How can employers attract and retain different categories of workers in a decentralised workforce?

2. Do we need to update the Work Design Manual for 2025?

3. For knowledge work, who will the development of generative AI impact the demand for freelancers?

4. What is the impact of ‘the Firm’ and what new types of organisations might emerge from the decentralised workforce?

5. What new services and markets do freelancers need, and who are the emerging providers to watch?

Work is not disappearing—it’s transforming.

To thrive, we must reimagine its structures and purposes in a decentralised, digital world.

Let me know your views.

Your platformer,

Andy

In Japan, more than 1 in 10 people are aged 80 or over.

With a shrinking workforce who will care for them?

This has massive implications for the economy, society, and poses a significant challenge for policymakers and employers.

This challenge will also be coming for China, Germany, Italy and other countries with an ageing demographic profile.

For the first time in history there are more people on our planet over 65 than kids under 5.

Looking East for signals about what is coming is standard fare for futurist types.

In this article you can read about cute cuddly seals, exoskeletons for care workers Society 5.0.

What can other countries learn anything from Japan on the ageing workforce?

The 2025 Problem

The thing about the ageing workforce is that you can see it coming a long way off.

Japan has been preparing and living with this for decades, but 2025 is when the country will really start to feel its age. This year, the total number of over-75s will reach nearly 22 million, up from 17 million a decade ago.

This wave of silver septuagenarians will ripple through Japan’s social systems, pushing up health and pension costs while the tax base shrinks.

It is estimated this cost will increase by nearly 60% between 2025 and 2040.

Japanese policymakers refer to this as the ‘2025 problem’.

Japan is already facing a labour shortage, and by 2040 it could be short of 11 million workers.

Riding the Silver Wave - Which Other Countries are Ageing Fast?

So why is this relevant to other countries?

It is not just Japan that has an ageing population. As you can see from this recent graph from the UN, 29% of the population is over 65 in Japan, followed by 24% in Italy, 17% in the US and 14% in China.

One challenge for society is the cost as health, social and welfare systems will buckle under this pressure. As I documented in a recent article,

the average retirement age has barely shifted over the past 50 years, hovering around 65 to 68. In the 1970s, a retiree might have only 10 years of retirement to plan for, in the 2020s, this is nearly twice as long. Retiring Retirement

Worker expectations are shifting rapidly due to the rising cost of living.

41% of American workers expect to work beyond 65, thirty years ago this expectation was 12%.

Employers are responding with schemes such as:

Grandternity leave, egg freezing, menopause benefits, and retaining retired staff as consultants, as seen at Unilever. (Read more in Living Better, not Just Longer )

What is ‘Old’ Anyway? Staying Genki in an Ageing World

As countries and employers grapple with an ageing workforce, it’s worth asking:

What does 'old' really mean in today’s world?

The Japanese gerontological association has suggested reclassifying those aged 65-74 as ‘pre-old’. In Japan many people remain genki, or full of life, long after they are officially ‘old’. More than half of those aged 65-69 and more than one-third aged 70-74 are employed. Most are healthy enough to live independently, with just 3% of those aged 65-74 require nursing care.

To examine quality of life and extend genki, it is also useful to look beyond the life expectancy statistics and look at changes to actual households. For example half of all households have one or more members over 65 and in 2020, 22 percent of women and 15 percent of men over age sixty-five were living alone.

So in a country famous for technology innovation and adoption of robots, might there be technology solutions to the ageing workforce?

My Robot and I

How might technology assist challenges such as loneliness, cognitive deterioration and loss of mobility?

Sony launched a cute robot dog called AIBO in 1999. In care homes, Aibo memorises user’s preferences, has a facial recognition systems, reacts to stimuli along 22 different axes.

If you want a proper cuddle with a furry companion, then PARO has the texture of a baby seal. It moves gently, makes realistic cooing sounds and looks at you with enormous eyes giving particular comfort to those who suffer from dementia, Alzheimer’s, and other cognitive disorders.

How will older people be cared for when there are less younger, stronger workers to care for them?

Japan’s main demographically driven technological trajectories cluster around two contrasting paradigms: augmentation of worker skills and automation of work.

To alleviate the task of lifting patients, some clinics use exoskeletons to give more strength and reduce fatigue. The exoskeleton used for medical use in Japan is called HAL (Hybrid Assistive Limb). It consists of a device that — when connected to the body of a person with mobility problems — detects the signals sent by the brain to the muscles and performs the desired movement.

Instead of the familiar worry,

'What if robots take our jobs?'

a better question might be,

'What if robots don’t take our jobs?'

So cuddle companions and expensive exoskeletons might help to a small extent, but are not going to alleviate the bigger issues of ageing society.

What else is Japan Doing to Support an Ageing Society?

In common with other countries, Japan is increasing the retirement age, from 60, and allowing more flexible re-hiring of retired employees.

And creating the workforce has been a challenge in Japan with low birth rates, which has been cited as 'the most significant challenge to the survival of the nation since the mid-nineteenth century'. There have been many policies to encourage more babies, in October 2019, the government introduced free preschool education and day-care services for children aged 3 to 5.

A challenge in many economies is to attract and retain more women in the workforce. Japanese tax and welfare policies discourage married women from working. When ‘dependent spouses’ earn less than 1.3m yen ($8,900) a year, they do not need to pay in to public-pension and health-insurance schemes. So more than 1.1m working women were limiting their working hours and earnings in order to stay under that threshold. The government has introduced subsidies to alleviate the effects of this so-called ‘income wall’.

To help seniors find new employment, the government operates 300 ‘Lifetime Employment Support Offices’ across Japan - dedicated to assisting senior job seekers in securing new opportunities.

There is a conception that Japan doesn’t welcome immigrants, however it has managed immigration with 2 million foreign workers in 2023. This is a 12% rise from the year before, was nearly triple the number a decade ago. A state think-tank in Japan reckons it needs an additional 2.1m foreign workers by 2030. That implies an 11% annual expansion, in line with the rate Japan achieved in the past decade.

Lifelong learning and upskilling is part of the solution - Prime Minister Kishida pledged $7.6 billion earlier this year to train workers for more high-skilled jobs in the next five years.

Japan has done some interesting work on managing rapid urbanisation. Surely it is easier to care for the over 80s when they are closer to expert facilities. Globally, more people reside in urban areas than in rural ones and that trend is set to continue, by 2050, two-thirds of the Worlds population will live in cities.

The Smart Platinum Society is not an exclusive members-only club, but a Japanese policy - an ‘age-free society’ which encourages citizens to live a fruitful ‘hundred-year life’, and leads to Society 5.0. Older persons are not considered senior citizens but are encouraged to stay healthy and to continue playing active roles in the labor force and society. It acknowledges that older persons constitute a growing market for goods and services and are a critical part of what is often called a ‘longevity economy’. The vision is for a ‘cyberphysical’ system, where digital tools and tech are fully integrated in people’s lives, and repurposing schools into care facilities for older people.

So there are some technological and policy approaches from Japan, to an ageing workforce. But before we try and copy, a few thoughts on the implications for the future of work more globally.

Lost in Translation - What Can We Really Learn from Japan about the Future of Work?

So some interesting ideas from Japan around using robots and fairly mainstream solutions about casting the workforce net more widely from abroad and within the existing internal labour market.

What can we reasonably learn from Japan?

Since the 1980s it has been fashionable to look to Japan for signals of what is coming to other developed economies and societies.

Before we extrapolate too wildly, it’s worth considering a few caveats.

Each country has its own unique demographics, labour markets, and culture. Looking to other countries and the history can give useful signals, but as Noah mentions, they are mostly wrong.

Looking ahead 20 years, I think we need to solve the problems of ageing workforce in more radical ways to the past. We need to design our societies around the changing demands of citizens, but we also need a new social contract. We might not have full employment so need other mechanisms for financial security, meaning, a sense of community, and solving nasty social problems with inter-disciplinary teams.

Do give me your examples of how your company is adapting to the ageing workforce.

Looking East,

Andy

This week we’re going to explore the World Economic Forum ‘Future of Jobs’ Report 2025. Whilst not the only, or necessary best, it’s surely a very valid and thorough piece of research that has a broad point of view and solid methodology and database (1,000 companies, employing 14 million employees). Probably slightly more focused on mid-large size businesses than OPB (just coined it; ‘One-Person-Businesses’) or SMBs.

It’s mostly a reflection of what managers at these companies think, than employees. We know that this is not reflective of the whole picture, but it’s important to register this perspective to then be able to understand better areas of disagreement.

There’s some good news (creation of 170 million jobs by 2030 and bad news – displacement of 92 million current jobs) but also some interesting data points on things like Skill-based hiring, and % of work estimated done by machines versus humans (34%, so yeah humans will still exist in 2030).

Goal is to digest and comment this for you so you don’t have to wade through 290 pages and redundant info.

We’re going to cover:

• Drivers of labour transformation (70% of the global workforce will be from emerging countries, Digitalization is still lower than you’d think)

• Jobs outlook (Which categories of jobs are declining and which ones rising)

• Skills outlook (Are companies hiring based on experience or skills?)

• Workforce strategies (Who’s going to do the work, humans or machines?)

Let’s go!

💎 Key Drivers of Labour Transformation

This section analyses what companies deem as top drivers changing work.

(Un)surprisingly, broadening digital access is keeping its #1 position because even though we may not feel that in our immediate circle of context, we need to remember that ‘only’ 67.5% of the world’s population currently has access to the internet.

In addition, digital access means capability for some processes and bureaucracy to be digitalized, and you don’t need to look very far to see that still impacts both private and public organizations. If you think about it, the level of digitalization of businesses and processes is a good indicator of our human tendency to think that the future comes so much earlier than it actually does.

Economic, geo-political and climate factors are close runner-ups and are less seen stealing the ‘media’ stage than technology. Take an example: most tech companies will be mentioning AI dozens of times in earnings calls, but may not touch upon things like the rise of cost of living or disruption by climate change. While the former is a classic ‘frog in a boiling water’ problem, with inflation being a key problem for the majority of countries, the latter is even less visible. The question is: how can a vicious cycle of cost-cutting be sustainable and keep creating growth and prosperity all-things considered?

Demographic shifts are another interesting trend.

These have a direct impact on global labour supply: currently balanced between lower-income (49%) and higher-income (51%) working-age populations, this distribution is expected to shift by 2050, with lower-income countries projected to hold 59% of the global working-age population.

Geographies with a demographic dividend, such as India and Sub- Saharan African nations, will supply nearly two-thirds of new workforce entrants in the coming years.

Some have also predicted that aging high-income economies with shrinking labour forces might increasingly look to deeper automation to counterbalance some of these demographic trends.

According to the World Bank, over the next 10 years, an unprecedented 1.2 billion young people in emerging economies will become working-age adults, while the job market in these economies is only expected to create 420 million additional jobs – risking leaving nearly 800 million young people in economic uncertainty

(This is a long article, if you prefer to read in a browser, click here)

💼 Jobs Outlook

In the next five years, 170 million jobs are projected to be created and 92 million jobs to be displaced, constituting a structural labour market churn of 22% of the 1.2 billion formal jobs in the dataset being studied. This amounts to a net employment increase of 7%, or 78 million jobs.

Seeing it this way, it may look pretty rosy. But even if the projection is based on a lot of assumptions and doesn’t take into account any ‘black swan’ event (who would have predicted the most recent wars or even technological ‘booms’?) the staggering truth is that the current trend is 30 million jobs lost per decade.

If we look at the specific job categories, we’re seeing very similar rankings to the past years. I think the current methodology is missing out on mapping also new categories of job roles, so maybe there’s some inspiration WEF can get from our last post on 35 New AI Roles to Watch out For? Just saying…

One chart I really liked was the one on human-machine division of work, where the highest jump is seen by tasks performed by technology on its own.

This is another reason why the category of ‘human-machine’ manager will be up and coming; not only will we need to create, innovate, assess AI systems, but also figure out how to optimize collaboration.

Will it be easier than optimizing collaboration between humans?

My 2c: less emotion and subjectivity won’t be able to compensate common sense and empathy.

This work does not consider the potential change in the absolute amount of work tasks (output) getting done. In other words, the argument is that both machines and humans might be significantly more productive in 2030 – performing more or higher value tasks in the same or less amount of time than it would have taken them to do so in 2025 – so any concern about humans “running out of things to do” due to automation would be misplaced.

Now, for the million dollar question: if an increasing amount of a firm’s total output and income is derived from advanced machines and proprietary algorithms, to what extent will human workers be able to get their share?

Are we going to look at Universal Basic Income or will companies be able to introduce some different incentive and compensation mechanisms so that employees don’t feel threatened or demotivated from this trend or even to contribute to its acceleration?

🔧 Skills Outlook

Interestingly, the ‘skill stability’ temperature has improved since 2020 (Covid) and it can be due to two main reasons:

1) Some reskilling and upskilling has already started/taken place

2) We’re seeing much more realistic truths about what AI can and can’t do. I’m going for the second option.

As for core skills, it’s not big news but it’s important to highlight how so many of the top ones are soft/human centered skills that are poised to have a huge huge comeback.

Human vs GenAI capability benchmarks are the new black, as we’re now seeing every advancement in LLM models being accompanied by a press release showing how it compares to performance by professionals in each specific area.

📦 Workforce Strategies

Now onto the big needs for organizations in the next five years.

Focus on 2: Organizational culture and resistance to change. This is huge, for two reasons. The first one, is that (I know, I’ve said it) it takes so much more time to adopt a new technology, much more than we’d like to think. Even worse if it’s changing every day and feels like a moving target. I mean, think about the internet and e-Commerce. It took a global pandemic to get a huge portion of businesses to get them to reconsider or accelerate their digital transition. Resistance to change, is human. That argues for more data-driven and structured HR work.

Skill assessments are also a hot topic. This is because work experience becomes harder to measure and more commoditized. Gone are the days where having ‘digital’ experience used to be a rarity, now each one of us is competing against hugely upskilled and economically competitive workforces in emerging economies. India is a talent power-house, and after taking some of the hottest leadership roles in big tech, it’s now starting to take more and more of the other functions too.

This calls for pre-employment tests, both technical and psychometric to evaluate behaviour and fit. Removing academic degree requirements and conducting skill-based hiring is an increasingly recognized approach to expanding talent availability. All of this is taking over traditional credentials like university degrees, which are decreasing in importance year over year (and I wrote about this in ‘The Death of Degrees and the Rise of Company Universities’)

If you’re interested in more details and sections on specific regions and industries, go check out the full 290 page PDF document.

Download the full report here

💨 TLDR - Is this too Optimistic?

The report says that Job creation will dominate job destruction – which is fairly optimistic. Whilst I think that there are many reasons to feel that way (we published at least 5 good reasons to be optimistic about work in 2025) it’s tougher to say yet over the long run.

This report is still assuming the status quo with some tweaks - but what if AI really does radically carve out the middle-managers, knowledge workers, designers, HR, marketeers?

Work needs to be redesigned both for individuals and for organizations, with a scientific and rational approach - something that doesn’t feel to be happening at the moment. There’s a lack of specific skill sets in the right places, but it’s also hard to stop a moving train. That’s why the real game-changers will be AI ‘Native’ companies and individuals, much like it’s happened with other technological revolutions.

There’s a big lag, and the world is increasingly becoming polarized and bi-modal. On one side, we'll see companies embracing AI and automation, redesigning workflows to leverage these tools, and upskilling their workforce to thrive in this new environment. These AI-native organizations will be agile, data-driven, and highly productive, with employees empowered to focus on high-value tasks and creative problem-solving. On the other side, organizations resistant to change will cling to outdated processes, ill-equipped to compete in this evolving landscape. Their work will likely remain stagnant, characterized by inefficient practices, low employee engagement, and a growing disconnect from the evolving demands of the market. This divergence will further deepen the divide, leaving those who fail to adapt increasingly behind - and many categories of roles will change because of it.

Tune in for more on this topic (how to design a new generation of work) in the next editions!

Ciao,

Matteo

This week I’d like to start a new column that is focused on new job roles and skills that AI will be creating. Some are already existing, some are educated hypotheses, or even better - ideas and demands that should be part of future regulations and governance.

Let’s dive in!

There’s a big rush to build (or hire) agents, as we discussed in our Will Digital Workers Revolutionise Work? article. But as AI starts to be more integrated and ubiquitous in companies, we’re going to start to see five macro-trends:

Specialization, Human-AI Coordination, Augmentation / Innovation and Cross-Discipline.

This will lead to completely new job roles, like ‘Chief AI Officer’ (Companies including Boeing, NASA, PwC, Pfizer have recently appointed one) but also to augmented/enhanced existing roles.

We’ll dive into each with some examples both current and upcoming.

1. AI Specialized Roles

These are roles that focus on very specific parts of AI, similar to how web development has evolved over the past two decades. In the early 2000s, a ‘webmaster’ was often responsible for all aspects of a website. But as web technologies became more complex, we saw the emergence of specialized roles like front-end developers, back-end developers, UX designers, and DevOps engineers. The same will apply to AI.

Example Roles:

• AI Ethicist: Responsible for ensuring that AI systems are developed and used ethically, AI Ethicists address concerns related to bias, privacy, and accountability. This role is crucial in building trust in AI systems among employees and stakeholders, and includes a focus on data (how it’s obtained, how it’s used)

• Prompt Engineer: Often referred to as “AI translators,” Prompt Engineers optimize user interactions with AI systems by crafting effective prompts that guide AI responses. This role bridges the gap between human communication and AI comprehension.

• AI Security Specialist: Focused on protecting AI systems from cyber threats, this role requires knowledge of cybersecurity principles and AI-specific vulnerabilities. As AI systems become more central to operations, this role will be critical in maintaining system integrity.

• Conversational AI Developer: They create chatbots and virtual assistants that can engage users in natural language, enhancing customer service experiences. This role is at the forefront of human-AI interaction.

• AI Solutions Analyst: This role involves analyzing business needs and developing tailored AI solutions to improve operational efficiency across various sectors. It requires a blend of business acumen and AI expertise. Currently being catered to by consulting firms, but I predict it’s going to be an internal role soon.

• AI Policy Advisor: This new role addresses the unique regulatory challenges posed by AI. Issues like algorithmic bias, AI transparency, and the societal impacts of automation are at the core of this job. It might help shape regulations that ensure AI systems are deployed responsibly, balancing innovation with ethical concerns.

• AI Auditor: This one needs to assess whether an AI system’s decisions are explainable, check for potential biases in training data, or ensure that AI systems comply with data privacy regulations. This role is crucial in building trust in AI systems across industries.

• AI Data Quality Specialist: Unlike traditional data quality roles, these specialists focus on the unique data needs of AI systems. They ensure that training data is not just accurate, but also representative and free from biases that could skew AI outputs. They might develop new methodologies for assessing data quality specifically for machine learning applications

2. Human-AI Roles

These are going to be completely new type of roles that will fall into different kinds of categories, but all aimed at improving the way that humans interact, guide and improve AI. AI-Human Collaboration and AI-Human Co-Creation are going to be extremely hot topics for the next few years, so more to come on this space.

• AI-Human Interaction Designer: This role goes beyond traditional UX design, focusing on creating interfaces where humans and AI can collaborate effectively. They design systems where AI enhances human capabilities rather than simply automating tasks. For instance, they might create an AI writing assistant that doesn’t just correct grammar but offers creative suggestions, adapting to the user’s writing style over time.

• AI Trainer/Curator: This role involves training AI models by providing them with high-quality data and refining their algorithms to improve performance. The skills developed here are essential for ongoing AI optimization, but it’s data labeling from humans. Startup Pareto seems to be doing this, claiming to have ‘The top 0.01% of AI data labelers’ and work with ‘elite teams’. Honestly, this looks to be like the new ‘back-office’ kind of work we thought would go out of the window, but would be happy to know if it’s otherwise.

• AI Explainability Specialist: This role emerged from the need to understand AI’s “black box” decision-making processes. Unlike traditional systems analysts, they work on making complex, often opaque AI algorithms interpretable to humans. They might develop tools to explain why an AI made a particular medical diagnosis, ensuring doctors can trust and verify AI-assisted decisions.

• AI Agent Manager: Similar to the Interaction designer, this role will automate workflows at first but then manage a swarm of agents and their performance. Setting them up, and making them work together efficiently.

A lot of new C-level roles will need to focus on Human-AI Collaboration. We’ll see both entirely new positions and existing roles with expanded responsibilities. New roles might include the ‘Chief AI Officer’ (CAIO), focused on AI strategy and implementation. Existing C-suite positions will evolve: CEOs will need to leverage AI for competitive advantage, CTOs will integrate AI into core infrastructure, CHROs will prioritize AI talent management, and CDOs will enhance data governance for AI initiatives. Additionally, the Chief Innovation Officer (CINO) and Chief Experience Officer (CXO) will take on critical roles in driving AI-related innovation and using AI to enhance customer experiences, respectively.

• Chief Human-AI Integration Officer: Senior role with focus on integrating AI into human workflows. They oversee the development of human-AI collaboration strategies, ensuring that AI augments rather than replaces human capabilities. They might develop programs to reskill employees for AI-enhanced roles, design human-AI teamwork protocols, and measure the impact of AI integration on workforce productivity and satisfaction.

3. Innovation and Augmentation: AI Enhanced Roles

As AI roles become more specialized, AI will make other roles more generic again (an application of the ‘bundling/unbundling’ cycle theory to the job market) therefore eliminating the need of having ultra-specific roles. This happens through the augmentation and automation of skills and tasks. While I don’t think we’ll get back to the ‘webmaster’ as above, many roles that have become too narrow will need to be more generalist and broad / eclectic again: think of a Social Media Manager (role didn’t exist in 2010) who may be very narrow in their capabilities - AI will enable that role to also be more self-sufficient with design, copywriting, paid media and so on. Some roles will be simply ‘enhanced’ by AI, meaning some parts of the job will be led by AI, but the remit and goals won’t be hugely different.

• AI-Augmented Creative Director: Unlike traditional Creative Directors, those in this role leverage AI as a creative partner. They use AI to generate and iterate on ideas at a scale impossible for humans alone. For example, they might use AI to generate thousands of ad variants, then apply their human creativity to refine and select the best options, blending machine efficiency with human insight.

• AI-Powered Financial Analyst: These analysts go beyond traditional data analysis, using AI to process vast amounts of unstructured data and identify patterns invisible to human analysts. They might use AI to analyze social media sentiment, satellite imagery, and traditional financial data simultaneously to make investment decisions, a task too complex for traditional methods.

• AI-Enhanced Customer Service Representative: New customer service reps will work in tandem with AI systems. They handle complex, empathy-requiring situations while using AI to access vast knowledge bases instantly through advanced Knowledge Management Systems. For instance, they might use real-time AI analysis of a customer’s tone and history to tailor their approach, providing a level of personalized service even if they hadn’t spoken with that customer beforehand.

• AI-Powered Content Creator: These entrepreneurs use AI tools to produce, optimize, and distribute content at scale. They might use AI to generate article drafts, create variations for different platforms, and even predict trending topics. For example, a YouTuber might use AI to generate script ideas, edit videos, and optimize thumbnails, allowing them to produce high-quality content at a pace previously requiring a full production team.

• Innovation through One-Person Businesses - we mentioned this recently, and stand our ground that this type of role will grow, both as a desire and also as a necessity as some jobs are displaced and GenZ look for their purpose and mission (or loyalty to corporates lowers, layoffs become more frequent and so on). I guess it’s a stretch to call this a new role, it’s merely another word for entrepreneur, but I do think it will have a different description and it’s mentioning. AI will empower this category further by enabling further automations and skill development (we wrote about this in Building Your Own Digital Workforce)

Another category of innovative role is going to focus on virtual worlds and avatar / fake personality creation. This emerging category represents a fusion of AI technology, content creation, and social media influence, and these roles involve creating, managing, and monetizing AI-generated personalities that can interact with audiences across various platforms.

• AI Personality Designer: These professionals craft the personas, backstories, and visual representations of AI-generated characters. Unlike traditional character designers, they must consider how these personalities will evolve through AI-driven interactions. For example, they might create a virtual influencer for a fashion brand, designing not just their look, but their personality traits, interests, and how they’ll respond to different scenarios.

• Virtual World Builder: As AI influencers become more sophisticated, these professionals will create immersive digital environments for them to “live” in. They might design virtual homes, workspaces, or even entire cities for AI personalities to inhabit, enhancing their perceived realism and engagement potential.

Challenges and Final Consideration

Yes, reskilling and upskilling will be a challenge. However, unlike in other technological revolutions, we must consider one big difference: the ability to do all of this through natural language. In the past, when a traditional accountant needed to upskill to work on Excel, they had to learn a new interface, understand formulas, and grasp complex functionalities. It was a steep learning curve that often required formal training or courses.

But with AI, particularly large language models, the barrier to entry is significantly lower. An accountant today can interact with AI tools using natural language, asking questions like “How do I create a pivot table for this data?” or “Can you help me optimize this budget spreadsheet?” The AI can provide step-by-step instructions, explanations, and even generate code or formulas, all in plain language.

This accessibility doesn’t mean that deep expertise isn’t valuable - it absolutely is. But it does mean that the initial hurdle of adopting new technologies is much lower. It’s like having a patient, all-knowing colleague always ready to help and explain things in a way you can understand.

So, while AI presents a more accessible path to upskilling than previous technological revolutions, it also requires a shift in how we approach learning and skill development. That’s why the role of HR will be even more important than before. The focus will be less on memorizing procedures or mastering specific tools, and more on developing adaptability, critical thinking, and the ability to effectively collaborate with AI systems. Plus, we will need to do a lot of change management to adapt culture, mission and values to new organizational models too. We need to develop new frameworks for performance evaluation, compensation, and career progression that align with an output-based model.

For example, one of the most crucial shifts required is moving from a time-based performance model to an output-based one. This transition is not just a matter of changing metrics; it represents a fundamental reimagining of how we value and measure work. This shift is already being modeled by AI-driven systems and AI Agents, which are typically compensated based on their outputs rather than the time they take to complete tasks.

Businesses are starting to use AI language models or automated design tools and not paying for the milliseconds of compute time, but for the quality and quantity of the output produced. This output-centric model is going to influence how we think about human work as well.

Lots more to come on this topic!

Ciao, Matteo

Happy New Year!

As we step into 2025, the world of work offers a wealth of exciting opportunities and positive shifts.

From global hiring trends to kitchen-table entrepreneurs, here's why there's reason for optimism in the year ahead.

Global Hiring Outlook: Growth Signals for 2025

The Net Employment Outlook (NEO) is a widely used measure for assessing future job prospects and hiring trends. It’s a forward-looking metric that provides hiring intentions of employers over the next quarter. It’s the difference between the percentage of employers planning to increase their workforce and those intending to decrease it. It’s based on interviews with over 40,000 employers across 42 countries.

The global Net Employment Outlook (NEO) stands at +25%, signaling that more employers plan to expand staffing in early 2025 than reduce it. This positive outlook suggests businesses are optimistic about growth, which can lead to more job opportunities worldwide.

The strongest hiring outlook comes from:

• India +40%, U.S. +32%, UK +28%, Asia Pacific +27%, Europe/EMEA +19%, Sweden +17%

Industries with strong hiring intentions include:

• IT +37%, Financial Services +33%, and Healthcare +28%.

We can use the NEO to ‘feel’ the pulse of global hiring intentions, helping us navigate the landscape of work in 2025.

While it’s not quite a ‘truth-revealing tool,’ it offers valuable insights into employer optimism and labour market shifts. That said, it reflects intentions rather than outcomes and excludes informal jobs, which are becoming an increasingly significant source of work.

As we turn from hiring intentions to another key economic indicator, there’s more reason to approach 2025 with cautious optimism.

The Misery Index is Low

The good news is that the Misery Index is historically fairly low.

The Misery Index sounds like a song by The Smiths, but it is in fact

an economic measure designed to gauge discomfort at a national level.

The Misery Index = Inflation + Unemployment

This simple formula combines the inflation rate (the rising cost of living, such as paying more for food, which makes us miserable) with the unemployment rate (not having an income also makes us miserable). The concept was introduced by economist Arthur Okun in the 1960s.

At about 7, the Misery Index in the U.S. today is relatively low compared to its historical highs, such as the 1980s, when it soared over 20 during a period of stagflation (high unemployment and high inflation). There is a similar value for the Misery Index for the UK, where inflation has eased, and unemployment is hovering at 4.3%.

However, before we crack open the supermarket cava, we must acknowledge that the Misery Index is a crude measure.

For example:

• Unemployment might be historically low—about 4% in the U.S. but this doesn’t capture the millions who are economically inactive because they are too sick to work. In the UK alone, 2.6 million people are economically inactive due to long-term illness, a record high.

• Having a job doesn’t eliminate poverty. In the UK, 17% of individuals in poverty come from households where all adults are working. This means even families with steady employment can struggle to pay for essentials like housing, energy, and food.

So, while the Misery Index gives us a reason to be cautiously optimistic about the economic outlook, it misses the bigger picture. The real challenge is to create not just jobs, but societies that provide economic security and opportunity for all.

Workforce indicators are one measure, but what about workplace flexibility and comfort…

The Pyjamification of Work

You might be reading this article sitting at your kitchen table wearing your pyjamas.

Remote work has surged since pre-COVID times, with U.S. remote workdays growing nearly fourfold (from 6.5% to 25%) and the UK seeing an eightfold increase.

The top 4 benefits of working from home according to research from Professor Nick Bloom are

1. No commute

2. Save on travel and lunch costs

3. Flexibility over where I work

4. Less time getting ready for work

The last two benefits clearly illustrate the Pyjamification of Work.

Flexibility to choose where to work:

At the office - deciding what to wear, getting dressed, make-up, driving in a traffic-jam, getting on the metro, buying a $10 coffee, and entering the office with a faux smile.

At home - we can respond to emails, code, edit, research wearing our pyjamas with our hair not brushed, and the face we woke up with.

It’s worth mentioning that not everyone can work from home in their pyjamas.

In the United States, around 75% of workdays are on-site; in the UK, about 60% of workers are primarily on-site, and in Sweden, at least 75% work mostly on-site.

And wearing your pyjamas whilst working in the hospital or driving truck is still not socially acceptable.

Further reading - you can read Professor Bloom’s latest report ‘The Future of WFH’ - 32 page PDF presentation

From having a boss who impacts what you wear at work to new earning opportunities without a boss…

More Ways to Earn

AI and digital tools are unlocking unprecedented opportunities for individuals to thrive outside traditional employment models, creating a new era of empowered solo entrepreneurs.

Back in 2020, I wrote Unleashing the Decentralised Workforce (still the most popular article here), in which I described how an untapped workforce could leverage digital infrastructure to achieve its economic potential outside of traditional employment.

I highlighted examples of livestreamers, playbourers, hourly experts, and slashies.

Sam Altman, the CEO of OpenAI, made an intriguing prediction that one day a single-person company will be worth a billion dollars thanks to AI technologies.

Instagram famously had 13 employees when it sold to Facebook for $1 billion in 2012.

A billion might be out of reach for most people, but what about a single-person company being worth a million dollars?

We gave some characteristics of one-person million dollar businesses in our 5 Big Questions for Work in 2025

Another category in my article was Digital Shopkeepers, where I mentioned Shopify, who enable people to run their own shops online and offline. Back in 2020 they had 1 million shopkeepers, now they have 5.6 million, with over $7 Billion revenue.

Some ideas for million-dollar businesses:

• Create an online course - Jack Butcher’s course Visualize Value generates more than a million a year.

• Start an AI-powered business - to cut spending, scale processes and increase margins. Examples include, SaaS platforms, personal assistant services, AI agencies, content marketing.

• Start a YouTube Channel - Nastya earned $28m last year from 124m subscribers - she is 10 years old.

• Reach $1 million with subscription revenue like Lenny’s Newsletter

Here are more million-dollar business ideas from Shopify.

More Ways to Learn

When you are likely to be working for another 50 or 60 years, taking a 4-year degree in the city you were born in when you are 18 makes less sense.

Nobody can really predict what type of education and skills will be useful as life will change radically in a decade, let alone a lifetime.

The rise of MOOCs (Massive Open Online Courses) and free online resources makes continuous learning more accessible than ever, helping individuals stay ahead in a rapidly changing world.

Here are 1,700 Free Online Courses from Top Universities such as Stanford, MIT and Oxford. Top up your knowledge with courses, and lectures on:

• How to Speak by Patrick Winston (AI pioneer) from MIT

• People Analytics from the University of Pennsylvania

I have used ChatGPT to help practice my Portuguese, devising lesson plans and conjugating most common verbs. This language teacher gives some more practical examples in a video to learn any language using ChatGPT, and I also found this article had some useful tips too.

And YouTube (for me - recipes and ‘how to fix something’) Khan Academy, and freeCodeCamp are well known sources of learning.

Whether you're coding in your pajamas, building a solo enterprise, or continuously learning new skills, the possibilities have never been more diverse.

For those in the workforce the key metrics we've explored - from the NEO to the Misery Index are fairly positive.

With the rise of remote work, AI-powered learning, and digital entrepreneurship, more people can participate in the future of work, regardless of their location or background.

The best opportunities often lie at the intersection of these trends.

Can you combine remote work flexibility with continuous learning?

Could your expertise become a million-dollar solo venture?

With the tools, platforms, and opportunities at your fingertips, 2025 offers the perfect time to take control of your work story.

How will you make this year your most impactful yet?

Starting the year positively,

Andy

Here are the most popular articles from 2024 on Workforce Futurist and Work 3 Newsletters. We know that each one had over 10,000 views, and a few of them started some interesting conversations about work.

Thanks to everyone who read, shared, commented, challenged…

See you in 2025!

1 - Death of the CV

Read the full article here.

2 - Building a Better World Without Jobs

What if the end of jobs as we know them isn’t a dystopian nightmare but an opportunity to redefine purpose, productivity, and prosperity?

This essay explores the radical shift from 'jobs' to 'value creation,' challenging the very foundations of our work systems.

If you’ve ever questioned why jobs exist—or imagined a world without them—this one’s for you.

Bertrand Duperrin gave his observations (en français) on the article.

Let us know what you think…

Read the full article here, or a video of my 20 minute keynote speech.

The article was also translated into spanish in two parts by Salvador Lorca.

3 - The Next Wave of AI: Building Your Own Digital Workforce

What if you could deploy a team of AI-powered digital workers tailored to your needs? This article dives into the cutting-edge world of personalised AI agents, exploring how they’re set to transform productivity, creativity, and the way we think about work itself. Ready to build your own digital workforce?

Read the full article here.

4 - Beyond Prediction: Shaping Your Work Story in an Uncertain World

This article, based on academic research, unpacks seven powerful narratives driving change in the workplace.

From ‘trusting the algorithm’ to ‘human augmentation’ and ‘job destruction’.

Which story do you believe, and which will you help write?

Read the full article here.

(This is a longer article, you can read it in full on a browser here.)

5 - Retiring Retirement

So far we have trashed the CV/resume, the Job, and explained why there is no future of work! Next up, we challenge a fairly successful but very 20th century invention, Retirement.

the average life expectancy of women in France, Italy and Japan is past 86 years.

the time from official retirement to actual life retirement is getting bigger every year and society can’t fund it anymore.

So what does an individual do? Read about some hot 🔥 strategies to have a healthy and fulfilling life in the 21st Century…

Read the full article here.

6 - Reinventing Work for the Digital Golden Age

There have been five tech revolutions in the last 250 years.

When new technology is introduced we typically have excitement, investment, and some wild extrapolation, Capital and Labour shift positions uncomfortably, riches are made, there is new hope, then there are the riots.

What can we learn from history to use technology to build a Golden Age of Work?

This article outlines five different pathways, from a decentralised work-matching infrastructure to lifelong career investment, and new mechanisms for financial security.

Read the full article here.

7 - Will Digital Workers Revolutionise Work?

Meet Alice and Jordan—AI digital workers poised to redefine the workplace.

In this article, we explore how these next-generation digital colleagues are transforming industries, challenging traditional roles, and sparking a revolution in how work gets done. Are you ready to collaborate?

Read the full article here.

8 - The End of Typing

Imagine a workplace where keyboards are relics of the past.

This article explores the rise of voice AI and how it’s revolutionising communication, training, and creativity. As typing fades into history, what new opportunities will this shift unlock for the future of work?

Read the full article here.

9 - 2034 – A Day in the Working Life

Every morning, as my neural notification device gently awakens me to optimal alertness, I'm grateful for our new education system. We each receive a Lifelong Learning Wallet – a personal education budget tied to our Digital Identity that provides ₿10 million (equivalent to $1 million today) for developing skills, or knowledge…

A working day in 2034 - with Equity Tokens, Lifelong Learning Wallets, and intelligent seamless Work-Matching Platforms.

Read the full article here.

10 - Planning for Non-Linear Career Trajectories

This article dives into a thought-provoking exchange between Paul Millerd, championing the unconventional path, and Al Dea, focused on structured growth. From redefining success to challenging the idea of careers altogether, this conversation will spark new ideas about careers.

Read the full article here.

We hope some of these articles resonated with you, if so let us know!

With festive joy,

Andy and Matteo

A couple of weeks ago we gave our view on some of the power dynamics shaping work in 2024, including:-

⛗ Capital 1 - 0 Labour – Mass layoffs masked by the dubious ‘AI inside’.

🤖 Robots 1 - 0 Humans– ChatGPT’s rise and its impact on knowledge work.

👩‍💼 Salarymen 0 - 1 Freelancers – More freelancers flood the market keeping rates low.

🌎 Anywheres 1 - 0 Somewheres – Remote work vs. location-based jobs.

📴 Technology 1 - 0 Trust – Who controls the apps we rely on?

🤳🏽 Centralised 0 - 1 Decentralised – A liquid workforce challenges the need for a large centralised workforce.

🏠 Office 1 - 0 Home – The RTO mandates that left remote advocates reeling.

We will be monitoring and documenting how these scores might change throughout 2025. As we discussed on our Webinar last week (which I forgot to record 🙈) here are some of the Questions for Work in 2025. Look out for opportunities to join more of our community webinars early next year.

1- How Will AI Agents redefine work?

In 2024 we scratched the surface of AI Agents, analysing some of the hottest startups developing AI workers (think of Business Development Reps, Customer Care). We also deep-dived on how each individual worker is going to have access to their own ‘personal’ workforce, customised on specific tasks, datasets and so on.

Developments in this area will see multimodal AI coming into play (voice/video interaction and assistance) and further evolution of capabilities.

However, we need to see how this type of usage will be coordinated by companies, or if it will be very fragmented and delegated to individuals. A recent study found that over 60% of employees ‘bring their own AI’ to work meaning that they are self-managing and choosing how to get that help.

Quality vs Quantity will become a real debate. We expect some of the hype coming down and starting to be confronted with reality: how much of the generative AI work is actually valuable or more similar to bad cholesterol ‘clogging up the web’s arteries’.

More quality will come through the real analysis and mapping of processes and workflows, and measurement of AI Agents will be a new type of KPI (and matter of startup/SaaS focus).

Unhealthy monitoring of employee productivity could come along with that. The pace of innovation and skill augmentation should be on the radar of HR teams which will need to augment themselves too.

2- Is the Office dead or just evolving?

The World’s 2nd largest employer with 1.5 million employees ordered their staff back to the office 5-days-a-week. Is a hybrid policy past its Prime?

Amazon want to reduce the number of managers, decrease bureaucracy and operate like the world’s largest startup. Many other large employers have followed suit including Uber, Twitter, Starbucks, Meta, Google, Goldman Sachs.

Is such a big move based on evidence that working in the office increases workforce productivity?

Not really.

Despite the work of tens of thousands of researchers who dedicate their careers to management practices, the translation of this research into corporate decision-making is very patchy.

Without robust, relevant external evidence to make a bold move on organisational change like this, the best available evidence is internal research conducted by analytics teams.

So what drives RTO mandates?

Power-politics or Corporate Peacocking 🦚 - a continuation of Command and Control management techniques developed for the economy of the 20th Century. “Productivity can only be maintained where I can see you working”

Real-estate investments - saving face with shareholders on a staggering $1.5 trillion US commercial real estate debt. Justification of new shiny glass offices and fine herbal tea stations. (Read more in Peak Office article.)

The rhetoric of RTO mandates can also provide linguistic cover for redundancies and lay-offs, where employers have over-hired and respond to technology innovation.

The use of the office is not dead, in fact office utilisation appears to be creeping up from the Covid-era, with some researchers reporting a 42% year-over-year increase.

In 2025, many knowledge workers will still work in offices for some days of the week, and of course some enjoy the social interaction.

The question in competitive industries is will this actually increase workforce productivity?

Workforce data that shows RTO mandates increase the flight-risk (attrition) for high-performers, women, and, millennials. (Source - Gartner). The rise of People Analytics in HR functions (see David Green’s excellent monthly Best HR & People Analytics articles) has seen two-tier organisations, those with, and those without a savvy analytics function. Traditional HR departments continue to struggle with employee relations and human supply chain issues.

3- How Will the Big Tech backlash impact work?

Traditionally a rich man seeking more influence will buy a newspaper:

• Marc Benioff owns Time Magazine

• Jeff Bezos bought The Washington Post in 2013

• Elon Musk didn’t buy a paper, he bought the whole publication platform, Twitter, for $43 billion.

We are now seeing Tech owners using their publications to sell messaging for their products. The issue is that their products will transform our societies which need a good quality debate. Not one where the agenda is led by narrow vested interests.

There are also anti-trust actions and lawsuits around the World against monopolistic tendencies of large tech firms. They need all the help they can get to defend their digital moats.

Musk employs about 135k workers directly, and his US Government Efficiency remit is 2.2 million civilian workers. Whatever happens with his public sector work and future job displacement, Shiny Tech will be both an enabler and deflector -> this will be a theme for Politics 2025.

Since industrialisation there has always been the fear that others will take our jobs.

‘The Robot will take our jobs’ is an old one. The word ‘robot’ was introduced in Karel Čapek's Czech play, ‘R.U.R.’(‘Rossum's Universal Robots’) over 100 years ago. The Czech word is based on a Slavic root ‘rob’ meaning ‘forced labor’. This fear has been remarkably easy to manipulate by interested parties whether the target is immigrants, robots or chatbots.

What might a Tech Backlash look like in 2025 in the World of Work?

Bossware fight back - workers are increasingly being spied on at work - from keystrokes to browser activity and location trackers. There is a worker fight-back using technology to fight technology. Where bosses are tracking mouse-activity as an indicator of work output (don’t get me started on this) workers have been buying ‘mouse🐭 jigglers’ devices that mimics movement to give the impression you are working.

Organised Labour - we had arguably the first ever workers strike against AI, by the actors and writers of Hollywood. Thankfully this was resolved fairly quickly and we can continue to watch Netflix-by-numbers boxsets. Workers protesting against the introduction of new technology is a classic plot in the history of work and how successful they are will depend on the latest Capital Vs Labour scores.

More bloody protests - society schisms are getting bigger along different fault lines, between the old and the young, the rich and poor, those who can work anywhere and those who need to work somewhere. There has always been anti-capitalism protests with some ominous recent signs these are becoming more bloody.

Platform Pilgrims - An exodus from social media platforms where the users object to the owner. Bluesky now has 25 million users as people gradually leave Twitter.

People just don’t want to work - 75% of Generation Z have no plans of going full-time for their whole lives - according to a survey in Dazed magazine.

4 - What are the opportunities for one-person million-dollar businesses?

Whilst some job tasks may start to be replaced, and some junior / entry level roles be replaced (or not hired for) in may areas, there’s a big potential upside to look out for: one-person businesses.

Already during COVID the rise of registered businesses skyrocketed, and we’ve seen the plethora of AI products being built in the last year, either using AI as the backbone, or using AI to help build / program the technology.

This is a huge shift / enabler / democratization of the startup process for three main reasons:

1 - Removal of Barriers to Entry: Less capital is needed to build, access to AI is cheaper than ever and will become even more so.

2 - Faster Time to Market - This access enables quicker iterations and development, meaning idea to product will take less time.

3 - More Efficiency and Flexibility - Either through Agents, self-augmentation (expansion of skills, i.e. the Founder that codes, markets and sells his product) or through the increasing use of fractional and freelance work, it will cost less to ship and to maintain companies.

Whilst we don’t have certainty on which ideas will be the one-million dollar businesses, we’re sure of one thing: there will be many more attempts in this gold-rush like era, including many ‘pick and axe’ kind of services. Much like in other technological advances (and bubbles) many will die on that hill but useful learnings will come, and be used for others’ success.

AI-powered ‘X’ for unexplored areas is going to be an obvious take, so the best advice is to go a level deeper and look for problems in the ‘real world’ through audience analysis in communities like Reddit, and turn product develop upside down: build the audience first, then test and the develop.

5 - What constitutes good career advice in 2025?

The average 20th-century worker studied for 20 years, worked for 45 years, retired at 65, then died 14 years later.

An industrial mindset told us when we could learn, earn and when we had to stop.

Today, the average life expectancy of women in France, Italy and Japan is past 86 years.

Babies born today in richer countries are expected to live past 100 years old

Thinking about careers in 2025 and beyond requires a different mindset to the last century.

Here are some thoughts:

1. Come on baby light my FIRE🔥 - strive for Financial Independence and take control of your work and finances. Whether you are 20, 40 or 60. Start saving up for a fund that pays your expected expenses when you stop getting paid for work.

2. Think like a business, $me myself.Ltd and I.Corp - develop your personal brand, develop a diverse sales pipeline, assess supply and demand etc

3. Do work that gives you fulfilment and aligns with your values - this is the goal isn’t it? Paid, unpaid, recognised, invisible. Retirement becomes irrelevant because you will not want to stop.

4. Stay healthy mentally and physically - obviously, but don’t burnout because of your work.

5. Develop 100 different careers and lives - we will be constantly working on projects, caring, convalescing, earning and learning at different rates. We will have 100 different careers and 100 different lives.

6. Commit to Lifelong learning - no school, mentor, university, employer will teach you what you need over a long life. Stay curious.

7. Nurture relationships and networks - the number of stable relationships a person can cognitively maintain is 150, according to research by Robin Dunbar. This number hasn’t changed in the era of social media. So have some fun finding ‘your people’, your tribe whether at work, in your community, online or IRL.

And finally, ignore all career advice.

There are many more questions for 2025 that we will be covering next year in our weekly updates.

What are your Big Questions for Work in 2025?

Write to us, or add to comments…

Happy Christmas! 🎄

Curiouser, and curiouser,

Andy and Matteo

We can’t predict what work will be needed 10 years from now.

We don’t know how many people will be needed to do that work.

The JOB has been a good form of wealth distribution since industrialisation.

However, many traditional jobs are now unbundling, enabled by technology.

Many are finding alternative ways to develop income from digital platforms.

Today, most people working globally don’t have a formal job.

We have a massive economic and societal opportunity to include an extended global workforce.

The institutions we have built up around the job will need to be changed and in some cases rebuilt.

This article outlines a brief history of the job, where it’s gone wrong and outlines what a brighter future of work might look like.

If you prefer, you can watch a video of a speech I did on the same topic, the link is below…

A Brief History of Work

The JOB is a fairly recent invention in human history.

Before the factories many worked in agriculture to feed their families.

We made pottery, weaved clothes, and brewed booze for fun.

Working from home is not new – for example, from the 1600s people in England made dresses and shoes at home.

With technology innovations such as the Bobbin and Spinning Jenny we could work together in factories.

And the modern JOB was created.

We then created the 12-hour working day, the 6-day working week, the 5-day week, the weekend, and guess what’s next?

We devised ‘command and control’ industrialisation.

We created laws, like closing the pubs early on Sunday nights so workers would turn up on time on Monday.

Our societies raised more taxes and provided welfare, education, and, health services.

As industrialisation advanced, public spending increased, as shown in this graph depicting Europe from 1870 to 2015. The graph shows the rise of the social state in Europe with public spending rising from 10% of fiscal revenues to 47%.

Workers got together collectively and campaigned for better pay and conditions and funded influential political parties.

For some, the JOB gave meaning, structure and purpose.

For most, it put food on the plates.

For two centuries JOBs, and the institutions we built from them, helped to redistribute wealth and improve education.

So far, Capital 1 - 1 Labour

For a more in-depth exploration, watch this 20 minute Video version of “Building a Better World Without Jobs”

Walls Come Tumbling Down

The Berlin Wall fell in 1989.

600m Chinese started to join the global workforce and were lifted out of poverty – the biggest improvement in history.

Globalisation accelerated on steroids.

Technology leaped into the internet era.

Wages in the West peaked around this time and then slowly declined.

The so-called ‘elephant 🐘 curve of global inequality’ ranks the world’s citizens on income. It shows how much each group standing on the same rung of the 1988 income ladder saw their income increase by 2008.

It shows the rise of a global middle class, including China, but also that 27% of total growth is captured by the top 1%.

This will be debated for decades by economists, but you get the point.

By the 2020s we have lower wages, populism, and a cost of living crisis.

Companies continue to use outdated ‘command and control’ techniques to manage their workforce, even when most don’t run factories anymore.

In a low-wage environment, the ‘employee engagement scam’ didn’t work - it was counterproductive. We now see mild worker protest with Quaint Quitting and reluctance to work.

A life of leisure? In the last 40 years, leisure time has actually fallen.

The reason is that more women have joined the workforce and this leisure time has been transferred to childcare.

The internet provided us with free streaming 24x7 entertainment. Now there are many competing ways to spend our leisure time outside work. From online gaming, shopping, socialising, or other streaming entertainment.

Workers have repriced their leisure time.

On the flip side, the internet gave us new digital landlords where we pay to access our own data, paying digital platforms 20% transaction fees for little discernable value.

In this era Capitalism got complacent and turned into it’s negligent cousin - Crony Capitalism – not the Adam Smith version.

Score Update - Capital 1 - 0 Labour

(This is a longer essay, if you prefer you can read in a browser here.)

Full-Circle - Technology innovation enables a transition back to a decentralised workforce

It was technology innovation that enabled us to centralise and industrialise work.

It is technology innovation that will allow us to decentralise work in smaller teams.

We have seen this in the Digital Covid Age.

In 3 months we accelerated 5-year digital transformation plans and budgets to enable remote working - now about 45% of US workers can work at home.

As the traditional job unbundles into tasks, it is redistributed on work-matching platforms.

A new infrastructure of work is being built that allows individuals to control their own career data and will make platforms much more effective and efficient. You can read more about a current critique of the work and staffing industry, how it will be rebuilt and what it means for the firm in my paper with a foreword by Don Tapscott.

There is a two-pronged attack on the structure of jobs - not necessarily the amount and type of work. By vertical industries, airlines, oil exploration, education, health, with specific work platforms, and across horizontal skills sets such as driving, design, coding, or coaching.

There are 3.4 billion people in the global workforce.

If a world without jobs surprises you, consider this,

Most workers do not have a job today.

6 out 10 workers do not have a formal job - they work in the informal economy.

They have no contract or much protection.

Digital platforms are allowing millions of people to develop income streams outside of traditional employment.

The platforms promise to be another massive economic and societal opportunity to include an extended workforce. From labourers to playbourers, carers to sharers, preachers to teachers. Read more about “unleashing the decentralised workforce”.

Building a New Social Contract and Infrastructure

Much written about this subject recycles economic theories developed before the digital economy and adds some dubious extrapolation.

The truth is nobody knows.

Let’s assume for now that,

1. There is less work to do

2. Employment contracts are changing

3. Wealth needs to be redistributed in different ways

Work doesn’t go away, but needs to be reframed, re-valued, and re-organised.

Financial security has disconnected from labour.

If there is less work to do then the aim for society is not full employment – but would seem to be greater financial security.

What do we need to build in a world with fewer jobs?

Here are the types of solutions we might see :-

Universal Basic Income is not a new idea, Thomas Paine was a fan in 1797. Basically, this gives every adult a minimum income. It has some free-market fans – it reduces public admin costs and privatises huge industries. Some on the left love it too. There have been some smaller-scale experiments with tenuous conclusions. The real test is when a whole country adopts it. It was a rather wonky idea three years ago, but then the pandemic hit. Globally, we learned we could pay people quickly when their income dried up and not everyone was paid by a monthly payroll. For example, the UK spent £70 billion on wage subsidies. Targeted furlough and kurzarbeit schemes have been proposed to prepare for future economic disruptions.

Broader scope of public services – with the increased cost of living, some governments are piloting public services that have historically been paid for by citizens. Recent schemes include free train services on specific lines in Spain and Germany.

Lifelong Education budgets - it is hard to know what skills children and adults will need in the future. Educational services will boom. But, how should this be funded? Maybe a solution is providing every adult with a learning budget to spend on courses and skill attainment.

We are talking about building a new social contract - how will we pay for this?

Taxation is one area where I have confidence that governments will respond to the challenge.

At one end of the scale, societies need to provide social protection, and at the other end need to enable better wealth distribution and encourage innovation and entrepreneurialism.

What A New Infrastructure For Work Might Look Like

New ways of organising work are emerging - we can start to see them now.

Employers don’t need to ‘control their workforces’ on multi-year contracts. Instead, they can source work directly from reliable work-matching platforms they trust. Less permanent salaried staff are needed and there is far more churn with lower transaction costs and smoother onboarding. The distinction blurs between salaried employees and all types of contractors.

For workers - there will be more projects, contracts, 100 different careers. Multiple income streams from equity shares on different projects. A guaranteed minimum basic income reduces financial insecurity for the times individuals cannot work e.g. caring for the family, full-time learning, when sick. Many will find like-minded people to collaborate with complementary skills and interests and flourish in smaller organisations.

The technology allows new teams to form. Allowing communities to flourish with governance, incentives, and share equity. Examples include DAOs, digital guilds, platform cooperatives, career-focused communities etc.

Workforce Futurist

How Work Will Rebundle Into Decentralized Teams

👋 This is the 19th regular update from the Workforce Futurist Newsletter – which is read by those on a journey to make work better. 📬 You can get future issues delivered to your inbox, just enter your email here…

Read more

5 years ago · 4 likes · Andy Spence

With increased financial security, we might see a boom in entrepreneurial activity.

The barriers to entry of joining a DAO, starting a business, sharing equity are lowered to zilch which makes it easier for collaboration.

Economic activity in the metaverse will grow with virtual concerts, games, auctions, sports, and community problem solving.

This can create a wealth boom for traditionally under-represented communities, not tied to a particular town, or a fortunate social graph.

Building a Preferable Future

JOBs have given us a lot, but we need to build new social support structures.

How long will this take?

A - Slow, slow, then quick.

B - Quicker than it took the West to industrialise.

C - I just don’t know.

Unemployment might not be something to be scared of, but to be embraced.

The 2nd part of Arthur C Clarke’s quote is about education,

We won’t die of boredom – creative humans will find innovative ways to entertain ourselves.

We might go back to pre-industrial days in some ways, but hopefully not in others.

Our range of future paths is not inevitable, it is shaped by us today.

We need to build a preferable future.

I can make a positive case for a better future of work, with or without jobs.

A ‘golden age for work’ is not inevitable.

As Carlota Perez warns…

“… there is nothing automatic about golden ages happening each time, just as there is nothing deterministic about a technological revolution.”

This account of the End of Jobs will scare the hell out of some and inspire others.

This will depend on where you’re at, and :

• the Capital you have in Chicago or Chengdu

• the Labour you provide in Lagos or London

• the Regulators you have in Riyadh or Rome

• the Unemployment support in Uppsala or Udaipur

The JOB will unwind slowly.

There will still need to be work contracts.

Employment patterns will change, enabled by technology.

But Work is not dead.

In fact, our most important work is ahead of us - to build better successful, more harmonious societies.

How do you see the future of jobs?

Building something better,

Andy

Finally - Join Our Webinar - The Big Questions for Work in 2025

And don’t miss our first-ever Work3 and Workforce Futurist Webinar:

The Big Questions for Work in 2025

Thursday 12th December at 2pm GMT, 9am ET.

We will be unpacking the big themes for work in 2025 and taking your questions live.

This exclusive 40-minute session is limited to the first 100 subscribers.

First come, first serve!

Register below →

Sign-up to our Event HERE

2024 was a year of power struggles—not just in the workplace, but globally.

With nearly half the world voting in critical elections, the balance of power between governments, citizens, and institutions shifted dramatically. These changes weren’t confined to politics; they echoed through workplaces, boardrooms, and digital platforms, shaping the future of how we work, live, and govern ourselves.

This review breaks down the seven key power dynamics that defined the year, drawing on the stories, trends, and articles that captured these upheavals. From layoffs and decentralised workforces to questions of trust and technology, the shifts in power at work mirror the political transformations we’ve witnessed globally.

In this review, we’ve broken down the key dynamics of the year:

⛗ Capital 1 - 0 Labour – Mass layoffs masked by the dubious ‘AI inside’.

🤖 Robots 1 - 0 Humans– ChatGPT’s rise and its impact on knowledge work.

🧑‍💼 Salarymen 0 - 1 Freelancers – More freelancers flood the market keeping rates low.

🌎 Anywheres 1 - 0 Somewheres – Remote work vs. location-based jobs.

🤳🏽 Technology 1 - 0 Trust – Who controls the apps we rely on?

🤳🏽 Centralised 0 - 1 Decentralised – A liquid workforce challenges the need for a large centralised workforce.

🏠 Office 1 - 0 Home – The RTO mandates that left remote advocates reeling.

Let’s take a closer look. Scroll down so you can sign-up for our free webinar next week, ‘The Big Questions for Work in 2025’.

Capital 1 - 0 Labour

Mass layoffs in 2024 became the dominant narrative, with ‘AI optimisation’ often cited as a convenient justification. The reality is that larger companies have over-hired in the past. The acceleration of technology automation (not just AI) is important, and also the freelancer market is more robust (because of the layoffs).

Back in May, Matteo reported 75,000 layoffs from 266 tech companies in Tech Layoffs Are Not Over, with some useful resources for tracking job layoffs.

One factor in the layoffs is technology automation, and it will be interesting to see the impact of AI Agents on employment, which we explored in Will Digital Workers Revolutionise Work? A look at AI tools replacing traditional roles and redefining productivity, and The Next Wave of AI: Building Your Own Digital Workforce - where we dig in deeper to how employees are building their own digital workforce.

Join Our Webinar - The Big Questions for Work in 2025

And don’t miss our first-ever Work3 and Workforce Futurist Webinar:

The Big Questions for Work in 2025

Thursday 12th December at 2pm GMT, 9am ET.

Matteo and I will be unpacking the big themes for work in 2025 and taking your questions live.

This exclusive 40-minute session is limited to the first 100 subscribers.

First come, first serve!

Register below →

Sign-up to our Event HERE

Robots 1 - 0 Humans

ChatGPT and its peers didn’t just assist humans; they started replacing them in key areas, automating knowledge work and reshaping workflows.

AI’s Next Leap: Human-Like reasoning and its impact on Employment - where we take a deep dive into GPT o1 which introduces ‘human-like’ capabilities.

Can AI actually make us humans even better?

In AI Vs Human Coaching, we highlight a number of apps that are beginning to coach people - remember however good they are today, they will be exponentially better with millions more iterations of data.

The ever-changing landscape of work demands a new approach to learning - we need to master learning velocity and skill agility to keep up with the machines.

Finally, here are 30 Startups Reinventing Work in 2024.

Salarymen 0 - 1 Freelancers

Corporate layoffs have created a surge of freelancers, swamping platforms and putting downward pressure on rates. Many more executives are trying Fractional Employment working part of their time for a company in a CMO or CFO role for example. Our article highlights some platforms for fractional work.

If you are looking for a new job, then AI can help too here are 20 Tools to Help You Land a New Job - from building your CV/resume to auto-applying for jobs.

Thinking about careers in 2025 and beyond requires a different mindset to the last century. One reason is that we are living longer and healthier ☺️, which gives employers and governments some challenges which we outline, in Living Better, Not Just Longer: Reinventing Work for the Silver Workforce. And for individuals we need to consider Planning for Non-Linear Career Trajectories.

Finally, we question whether we should be Retiring Retirement…

(This is a long post, you can use this link to read in a browser.)

Anywheres 1 - 1 Somewheres

While remote work allowed some to thrive from bedrooms to beaches, other workers were firmly rooted locally working in supermarkets, hospitals and fields.

The main fault line in contemporary society, according to David Goodhart, is not between right and left, or capitalists and liberals. It’s between the people who see the world from Anywhere and the people who see it from Somewhere.

One thing that can help in the future is by redefining what a company is, in relation to the communities they evolve from.

And finally, we are transported from 2024 to 2034 – A Day in the Working Life. With equity tokens, lifelong learning wallets, and intelligent seamless work-matching platforms…a personal view on the future, which it turns out depends on your past.

Technology 1 - 0 Trust

As apps and platforms took over some of our work functions, workers questioned whether their data and privacy were being treated ethically. Peoples’ tech preferences are now as passionate and specific as their food preferences. ChatGPT has become invaluable, but are we aware of the environmental and privacy downside? Matteo examines the impact of AI on mental health in AI Anxiety.

We suggest that technology will also enable us to kill off two old work tools that need to be in Room 102 in the Death of the CV and the keyboard in The End of Typing?

And what new technology means new practices, including the rise of Video - in the TikTokificaiton of Work.

Finally, a question, Is AI Truly Revolutionary? We give a 250 year perspective on tech disruption.

Centralised 0 - 1 Decentralised

Employers increasingly sought workforces that could flex with demand, leaning on freelancers and platforms over permanent employees.

What might a decentralised, and more equitable fairer world of work look like?

Reinventing Work for the Digital Golden Age – Five pathways towards a more inclusive and entrepreneurial future of work: from lifelong learning budgets to better work-matching infrastructure.

Office 1 - 0 Home

The return-to-office debate ended in a landslide for office landlords and corporate real estate, leaving remote work advocates out in the cold.

What was the real reason for RTO mandates, productivity, coffee machine chats, or trying to justify massive investment in corporate real-estate? Read Peak Office to find out - our 2nd most popular article of 2024.

And finally, by trying to include this photo of a proud peacock on the roofs of Lisbon in an article, I wonder whether we have heard The Last Call of the Corporate Peacock.

A Personal Note to Our 25,000 Subscribers

To everyone who subscribed, commented, shared, and engaged with us this year: thank you. You’re more than readers; you’re co-creators of this revolution in how we think about work.

Next year, we’ll be offering more opportunities to collaborate, network, and challenge outdated work paradigms.

If you are building a product or service that improves how we work let us know - we would love to see a demo.

Your Work3 and Workforce Futurist Team,
Andy and Matteo

Everybody has a ‘work story’ – what we do for a living and our career journey. 

Researchers have identified seven dominant narratives shaping the future of work.

Which of these will define your journey?

I’ll explore these narratives, look for signals to support them, and ask whether they really matter.

We can’t predict the future, but we can predict your prediction

In the study researchers asked experts to rank the likelihood of predictions and the year they were most likely to happen.  They landed on the sequence of events which included:-

2044: Surveillance societies become the norm worldwide

2051: Governments introduce universal basic income

2063: Technocratic elites start colonizing other planets

2065: All human qualities are surpassed by intelligent technology

So, nothing to worry about with these scenarios!

The researchers found that…

“…the tech entrepreneurs were radical optimists, the economists believed in rationality above all, and the authors and journalists held attitudes indicative of misanthropy and a belief that much in society is decided by those in power behind closed doors.” HBR.

This was an interesting finding - but maybe not so surprising. What I found particularly insightful was the authors summary of seven identifiable narratives on the future of work.

Seven Competing Narratives about the Future of Work

The authors identified competing narratives in terms of seven different schools, and below I have illustrated, amplified and added my take on each of them.  

1 - Dataism  - Trust the Algorithm

The Story: Data and algorithms dominate workplace decision-making, replacing human intuition.

Signals: Algorithmic management at Amazon warehouses. The rise of Digital Workers.

Extreme Future: The Quantified Worker – our personal AI agent tells us what to eat for breakfast based on our sleep patterns and work schedule…

My Take:  This assumes data is neutral and infallible. Without scrutiny, we risk building systems that reinforce biases and strip away human agency. We will see more Quantified Workers, but only at the speed of trust.

2 – Exterminism - The Robots Take Over

"The factory of the future will have only two employees: a man and a dog. The man will be there to feed the dog. The dog will be there to keep the man from touching the equipment." – Warren Bennis

The Story: Automation leads to catastrophic job losses, creating a dystopian future.

Signals: Hollywood’s first AI strike highlighted fears of automation in creative industries. Studies predict millions of displaced jobs by 2030.

Extreme Future: By 2050, mass unemployment dominates society. Governments scramble to create universal job replacement programs.

My Take: This assumes humanity is static. History shows we adapt, ecosystems evolve, new industries emerge, and societies recalibrate. We must push for systems that prioritise fairness to prevent such extremes.

3 -  Re/Upskilling - Learn or Be Left Behind

"The illiterate of the 21st century will not be those who cannot read and write, but those who cannot learn, unlearn, and relearn." – Alvin Toffler

The Story: Lifelong learning becomes a necessity to stay employable.

Signals: Singapore’s SkillsFuture framework for workers over 40. The rise of online education platforms like Coursera.

Extreme Future: Education becomes AI-driven, with personalised microlearning tailored to real-time job market demands.

My Take: Re/upskilling feels less like a narrative and more like a universal feature of all the others.

4 – Augmentation - Humans and Machines in Harmony

"The machine does not isolate man from the great problems of nature but plunges him more deeply into them." – Antoine de Saint-Exupéry

The Story: Technology enhances human capabilities rather than replacing them.

Signals: AI-powered surgical assistants improving precision. AI tools analysing case law.

Job Interview Question “I’m sure MIT was fun, but now tell me about the AI Agent team you use every day?”

Extreme Future: Brain-machine interfaces allow people to solve problems while dreaming.

My Take: This is a hopeful and practical vision. Machines as collaborators, not competitors should be our goal. However if only a few benefit, augmentation won’t fulfil its promise. 

5 - The Singularity - When AI Surpasses Us

Story: AI surpasses human intelligence, transforming society.

Signals: Futurists like Ray Kurzweil predict this by 2045. Philosophical debates about AGI dominate ethics boards.

Extreme Future: Super-intelligent AI demands citizenship. People upload their minds to live digitally.

My Take: Fascinating but speculative - let’s first agree what ‘intelligence’ means?

6 - Job Destruction - The Death of Low-Skilled Work

The Story: Automation disproportionately impacts low-skilled jobs, exacerbating inequality.

Signals: Retail jobs disappear with cashier-less stores like Amazon Go. Autonomous trucking pilots in the U.S.

Extreme Future: Governments issue ‘transition stipends’ to keep displaced workers afloat.

My Take: Automation is inevitable, but inequality is a choice. This narrative is compelling but incomplete. It doesn’t account for the historical resilience of economies in creating new types of work, or how labour markets unbundle tasks and then rebundle.

7 - Work Deintensification - The Age of Leisure

"The end of labor is to gain leisure." – Aristotle

The Story: Technology reduces work hours, leading to more leisure and creativity.

Signals: Four-day workweek trials around the World. Advocates like Andrew Yang champion Universal Basic Income.

Extreme Future: By 2051, UBI is standard, and governments mandate digital detoxes.

My Take: Culturally appealing, but when will we stop tying our identities to work? This is the utopian narrative I want to believe in, but work deintensification would require significant cultural shifts and systemic reforms, which I feel far off in many parts of the world.

You can read the full paper with a link at the end of the article.

Poll: What Narrative Do You Believe In Most?

📊 Which narrative about the future of work resonates most with you?

1. Dataism: Algorithms dominate decision-making, sidelining human intuition.

2. Exterminism: Robots and AI lead to catastrophic job losses.

3. Re/Upskilling: Lifelong learning is necessary to remain employable.

4. Augmentation: Humans and machines collaborate in harmony.

5. The Singularity: AI surpasses human intelligence, reshaping society.

6. Job Destruction: Automation erodes low-skilled jobs, deepening inequality.

7. Work Deintensification: Technology ushers in shorter workweeks and UBI.

Vote HERE

The Next Chapter of Research

While the study offers a useful framework, it’s not the complete story, as the authors admit. First, it’s based entirely on Belgian media, so they didn’t include Workforce Futurist! The seven narratives aren’t entirely mutually exclusive. E.g. Augmentation can be seen optimistically as an empowering narrative, while sceptics might question its feasibility or equitable access.

The Future of Work is What We Make It

The past was yours, But the future's mine.  The Stone Roses.

When it comes to shaping our future work story, it is far too easy to become distracted by the many competing narratives out there.

"These competing narratives are not just predictions; they’re choices. By recognising the signals and shaping your own work story, you can decide how these trends impact your future."

The media presents a very narrow view of the future of work – the perspective is too local, or doesn’t reflect economic history, and lacks context and imagination. In other words it is wrong!

They are also too many fear-based narratives.

As I mentioned in my article below, looking at the past 250 years featuring Carlota Perez’s research, a golden age for work will only happen when there are positive visions.

“we would like to invite every citizen, every policy maker, and every manager and CEO to enter the public debate around the future of work to ensure that it unfolds within a social and democratic dialogue. The future is what we make it.” HBR

The seven narratives are not mutually exclusive—you don’t have to pick just one.

For Workforce Futurists, I would suggest :-

• Understand and critique the dominant narratives in your industry

• Shape your own narrative

• Invest in flexible strategies for the future which,

• links to your own financial independence, health and welfare.

The question here is not

“What will happen?” but rather:

“What kind of future do we want to create?”

What’s your narrative?

Tell me in the comments, or hit reply.

Future Reading

• Dries, Nicky & Luyckx, Joost & Rogiers, Philip. (2023). Imagining the (Distant) Future of Work. Academy of Management Discoveries. You can download the full PDF article here

• My views on five pathways towards building a better future of work - which of the 7 narratives dominate? 🤔

• Capitalist Realism: Is There No Alternative? Fisher, M. (2009) This book critiques neoliberalism, framing the inevitability of current economic structures, and contributes to the understanding of the "There Is No Alternative" (TINA) mindset reflected in the narrative of Re/Upskilling.

• The Worlds I See: Curiosity, Exploration, and Discovery at the Dawn of AI" (2023) Fei-Fei Li. In this book, Dr. Li delves into her journey in AI, she advocates for AI systems designed to augment human capabilities rather than replace them.

• Future of Employment: How Susceptible Are Jobs to Computerisation?  Frey, C. B., & Osborne, M. A. (2013) This foundational paper examines the susceptibility of occupations to automation - influencing the somewhat flawed narratives of Exterminism and Job Destruction.

Writing the next chapter of my future,

Andy

When was the last time you barked orders at Alexa?

The way we interact with machines is about to change dramatically.

While most people are obsessing over ChatGPT and image generators, they’re underestimating the biggest paradigm shift: Voice AI.

Today, I'll show you why. We’re going to cover:

🗣️ Advanced Voice Mode: What is it

🗣️ Why Voice is the (obvious) last frontier of AI

🗣️ Key Applications in the Workplace

🗣️ Voice and Workplace Simulations

🗣️ Implementation Challenges and Solutions

🗣️ Top AI Voice Tools

Let’s dive in!

Death of the Keyboard

It's 2024, and our main way to work is still with a typewriter-like machine invented in 1714, with the keyboard invented in 1981.

Think about it: we spend our days translating our thoughts into finger movements, typing away on a device that was designed for a completely different era. While writing has served us well for millennia, and will continue to be crucial for many tasks, the emergence of advanced voice AI presents an opportunity to rethink how we interact with technology at work.

Sure, we've had Siri and Alexa for over a decade now, but their capabilities have largely been confined to simple commands like setting timers or playing music – barely scratching the surface of voice technology's potential. This shift in how we interact with technology mirrors a broader trend: just as TikTok has transformed how we consume content – making it more immediate, conversational, and natural – voice AI is poised to do the same for our work interactions.

We're moving away from formal, structured inputs (like typing emails or commands) toward more natural, fluid ways of getting things done. The 'TikTokification' of work (which we wrote about here and here) isn't just about short-form videos; it's about making technology adapt to our natural behaviors rather than the other way around.

Speaking is our most natural form of communication – we learn it from birth, use it effortlessly, and can convey nuance and emotion in ways that writing often struggles to capture. Until now, though, voice interfaces have been limited by technology's inability to truly understand and respond to natural speech. That's about to change.

Advanced Voice Mode: What is it

OpenAI released its Advanced Voice Mode on September 24, 2024, to a broader group of users, specifically targeting subscribers of the ChatGPT Plus and Team plans. This rollout followed an initial alpha testing phase that began in July 2024, which was limited to a select group of users due to safety concerns and the need for further testing. The launch also followed controversy between OpenAI and Scarlett Johansson, following Sam Altman’s proposal for the actress to be the official voice of this feature, following the acclaimed (and must-see) movie ‘Her’ that featured a ‘sentient’ AI operating system.

Advanced Voice Mode allows to generate human-like speech with unprecedented realism and naturalness. It incorporates nuances in tone, inflection, and emotion, making it nearly indistinguishable from human speech. With Advanced Voice Mode, AI can not only read text aloud but also engage in dynamic, context-aware conversations.

Why Voice is the (obvious) last frontier of AI

Now while this feature is currently only available in the ChatGPT application, it’s only going to be a matter of time until it’s going to be featured in third-party apps and be generally more ubiquitous.

Why? It’s simple; it removes the last barrier of ‘friction’ between a user and the technology. Just think about it - AI and LLMs were already available before 2022 (by the way; ChatGPT just blew its 2 year birthday candles just a few days ago, doesn’t it seem like much longer?). The craze started when usability (friction) barriers were removed by giving users a text box that gave them (everyone) the possibility to use it, much like it happened with Google in 1998.

Voice removes this last barrier by allowing users to interact with AI through natural speech, making the technology even more accessible and intuitive. This advancement opens up a whole new realm of possibilities for AI applications, particularly in the workplace, where voice-based interactions can significantly enhance productivity and collaboration.

Why you ask?

Because this is not just a technological advancement; it's a paradigm shift in how we interact with machines. By mimicking natural human conversation, voice AI can create more engaging and efficient workplace environments. For instance, it can facilitate hands-free operations, enable multitasking, and provide accessibility options for individuals with visual or motor impairments. Moreover, voice AI can potentially revolutionize customer service, virtual meetings, and even creative processes in the workplace.

(This is a longer article - clear here to read in your browser.)

Key Applications in the Workplace

Voice AI is revolutionizing workplace training across three critical domains: customer service, leadership development, and technical operations.

Each area leverages this technology in unique ways, creating immersive learning experiences that were previously impossible to scale. The results have been remarkable – from reducing training time by up to 50% to significantly improving employee confidence and performance.

Customer Service - In customer service, voice AI creates a safe space for representatives to master challenging scenarios before facing real customers. Imagine a new hire practicing with an AI that can seamlessly switch between an angry customer demanding a refund and a confused user needing technical support. The AI provides real-time feedback on tone, word choice, and emotional intelligence, while also measuring key metrics like clarity and pace. What's particularly powerful is the ability to simulate various accents and communication styles, preparing representatives for the true diversity of their customer base. One major retail company reported that after implementing voice AI training, their customer satisfaction scores improved by 23% within the first three months.

Leadership Development and Communication Training - Leadership development has seen equally impressive applications. Voice AI serves as a virtual coach, allowing emerging leaders to practice crucial conversations in a risk-free environment. From delivering tough performance reviews to managing team conflicts, the technology provides immediate feedback on leadership presence and communication effectiveness. The AI can simulate complex scenarios like budget negotiations or crisis management situations, allowing leaders to develop muscle memory for high-stakes conversations. More importantly, it helps them recognize patterns in their communication style and develop more effective approaches over time.

One example:

Nothing is going to substitute for the real thing, and from learning from real failures. But in so many environments, including sports, you get to train and practice before that match. This is the same idea, that can be possible and easier to implement by feeding this technology with context. Which makes me think that companies could/should even provide such datasets from the beginning, i.e. ‘Mike Smith’ so you can know how he interacts and practice. This should also be backed by science, by mixing it with the psychological / personality profiling (Big5, etc).

Technical Training - This domain showcases perhaps the most practical applications of voice AI. Engineers and technicians can now receive hands-free, step-by-step guidance while working on equipment, significantly reducing the risk of errors in critical procedures. The AI adapts to the user's pace, provides real-time troubleshooting support, and can even detect potential safety issues before they become problems. This has been particularly valuable in industries like manufacturing and healthcare, where precision and safety are paramount. For instance, a leading manufacturing plant reported a 60% reduction in training-related incidents after implementing voice AI guidance for their maintenance procedures.

Meeting Mediator - Voice AI can serve as an impartial third party in heated discussions, analyzing conversation patterns and emotional undertones in real-time. For example, it could notify participants when someone is being repeatedly interrupted or when the discussion is becoming unnecessarily confrontational. One tech company reported that implementing this reduced meeting tensions and improved participation from quieter team members by 40%.

Cultural Intelligence Coach - For global companies, voice AI can help employees navigate cultural nuances in communication. Beyond just language translation, it can provide real-time guidance on cultural context, helping professionals adjust their communication style for different cultural contexts. A sales team using this technology reported a 35% improvement in closing deals with international clients.

Wellness Monitor - Voice AI can analyze subtle changes in speech patterns to detect signs of stress, burnout, or declining engagement. This early warning system helps managers proactively address well-being concerns before they become serious issues. One organization using this approach saw a 45% reduction in stress-related leave.

Documentation Companion - Instead of traditional note-taking, imagine having a voice AI that not only transcribes but actively participates in the documentation process. While working on complex tasks, engineers or doctors could verbally explain what they're doing, with the AI creating structured documentation, highlighting potential issues, and suggesting relevant references or protocols.

Here's a real example that illustrates an innovative application:

These applications show how voice AI can go beyond simple automation to create new ways of working and thinking. The key is to look for areas where natural conversation can remove friction or create value in unexpected ways.

Voice and Workplace Simulations

Many of the areas above concern training and development, but now I’d like to talk about ‘simulations’. There’s two main types:

Job simulations - pre-employment assessments designed to evaluate candidates by having them perform tasks and scenarios that reflect the actual responsibilities of the job they are applying for.

Project simulations - during employment, these could be any type of simulations that anticipate how a new product can be built, risk assessments and so on. The concept is similar, but even stronger because it is not ‘just’ used to understand if the candidate is suitable to be hired, but used for:

• Skill Development: Employees can practice and refine their skills in a safe setting, allowing for experimentation without the risk of real-world consequences.

• Team Collaboration: These simulations often involve group work, helping to build teamwork and communication skills among employees.

• Realistic Experience: By mimicking actual project scenarios, employees gain valuable insights into project management, decision-making processes, and resource allocation.

• Feedback Mechanism: Participants receive immediate feedback on their performance, enabling them to identify areas for improvement and adjust their approaches accordingly.

Some examples include:

• Case Studies: Employees analyze real or hypothetical situations, developing solutions and strategies as they would in an actual project.

• Role-Playing Scenarios: Participants assume different roles within a project team to understand various perspectives and responsibilities.

• Cross-Functional Projects: Employees from different departments collaborate on a simulated project to enhance interdepartmental communication and cooperation.

• Virtual Simulations: Utilizing software tools, employees engage in digital environments that replicate complex project scenarios, allowing for exploration of various outcomes based on their decisions.

While traditional simulations often felt mechanical and scripted, voice AI brings a level of natural interaction that makes these experiences feel natural, and therefore more immersive and effective.

What makes this combination particularly powerful is its ability to create what I call "dynamic learning environments." Unlike traditional e-learning or role-playing exercises, voice-enabled simulations can adapt in real-time to the learner's responses, creating truly personalized training experiences. For example, if a sales representative struggles with handling price objections, the simulation can automatically increase the frequency of such scenarios while adjusting the difficulty based on their progress.

The impact of this technology becomes even more significant when combined with other emerging technologies. Some forward-thinking companies are already experimenting with voice AI-powered simulations in virtual reality environments, creating what some call "the closest thing to real-world experience you can get without real-world consequences." Imagine practicing a high-stakes board presentation where you can not only see your virtual audience but also interact with them through natural conversation, receiving immediate feedback on everything from your tone of voice to your body language.

Looking ahead, we're seeing the emergence of what I call "hybrid simulation environments." These combine voice AI with other technologies like AR/VR and motion tracking to create comprehensive training experiences.

Implementation

Implementing these types of simulations is not easy, there’s no current one-stop-shop or proper integration into the various Learning Management Systems just yet. It’s an early stage, where experimentation is key, so my suggestion would be to build an internal program where AI power-users map some of the above examples and how they could fit into the specific company and employees use cases, set some goals and milestones and see what sticks or doesn’t. I think the beauty of these tools is that they’re highly customizable, and could be part of the ‘agent’ workforce that everyone builds for them in the future.

That said, I’m totally skipping some key concerns and considerations that should be done about privacy of the data, ethics and so on. From my point of view they follow AI policy and regulation in general and should be put inside that basket.

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TLDR;

Voice AI represents more than just a new way to interact with machines – it's a fundamental shift in how we might approach work itself. From training and simulations to real-time assistance and cultural navigation, the applications we've discussed are just the beginning.

The key insight is this: every major leap in technology adoption has been marked by reduced friction between humans and machines. The mouse made computers accessible to everyone. The touchscreen made mobile computing intuitive. Now, voice AI promises to remove perhaps the last major barrier – the need to translate our thoughts into typed text.

However, this isn't about replacing writing entirely. Instead, it's about having the right tool for the right job. Writing will remain crucial for tasks requiring precision, permanence, and careful consideration. Voice AI will excel in scenarios requiring natural interaction, real-time feedback, and hands-free operation. It's an opportunity for employers to bridge the gap with younger workers in particular who expect to use similar technology from home and play in the workplace.

The organizations that will thrive in this new era will be those that thoughtfully integrate voice AI into their workflows, understanding both its potential and limitations. They'll create environments where employees can seamlessly switch between speaking and typing, choosing the most effective medium for each task.

As we look ahead, the question isn't whether voice AI will transform the workplace – it's how quickly and thoroughly this transformation will occur. The technology is here. The use cases are clear. The only remaining question is: are you ready to find your voice in this new world of work?

Ciao,

Matteo

After AI, the new buzzword will be ‘AI Agents’. That’s why today I’d like to talk about ‘Agent Engineering’ — a concept that will be seen as an evolution of software development. This brings us to a scenario where individuals, not only companies, can create and deploy their own army of AI agents for a wide array of personal and professional tasks.

Imagine if you had a team of personal digital assistants that can do research, schedule meetings, take minutes for you…

This is going to be fundamental change in how we interact with AI. It's moving us from being passive consumers of AI tools to active creators of personalized AI assistants. The great thing is we won’t need to be a machine learning expert or a seasoned programmer to get started.

Here's our agenda:

🌟 What is Agent Engineering — Defining the concept and its potential impact

🛠️ The Rise of Personal AI Agents — How and why everyone is becoming an agent engineer

🧠 Key Applications of AI Agents — From brainstorming to skill acquisition

🚀 Getting Started with Agent Engineering — Tools and platforms for creating your first AI agents

🔮 The Future of Work and Learning — How agent engineering might reshape our daily lives

Let's dive in!

🌟 What is Agent Engineering?

Defining Agent Engineering 🏷️

Agent Engineering represents a revolutionary shift in the field of AI, moving beyond simple prompt engineering to create autonomous, proactive AI systems. It's the practice of designing, creating, customizing, and deploying AI agents that can independently perform complex tasks, make decisions, and operate with true autonomy.

Imagine having a personal AI lab where you're both the scientist and the beneficiary of your creations. That's the essence of Agent Engineering.

Unlike traditional software development, which often involves:

• Writing code from scratch

• Working with complex frameworks

• Spending months on development and testing

Agent Engineering takes a different approach:

🧠 Leverages pre-trained AI models — Think of these as highly educated AI brains ready to be specialized

🛠️ Utilizes user-friendly interfaces — Making AI development accessible to a broader range of professionals

🎯 Focuses on task-specific assistants — Creating AI agents tailored to specific needs and contexts

Key aspects of Agent Engineering:

🔬 From Reactive to Proactive: Engineered agents can initiate actions, anticipate needs, and work towards goals independently.

🌐 Holistic Approach: Creates entities that understand their environment, set goals, and take actions to achieve them.

🎯 Purpose-Driven Design: Every AI agent is built with a clearly defined role, shaping its actions and decision-making processes.

🔄 Continuous Learning: Agents improve over time, learning from interactions and feedback.

🤝 Collaborative AI: Agents can work together to tackle complex, multi-step problems.

However, one of the big paradigm shifts is that AI Engineering is not ‘deterministic’; it’s probabilistic. This is because of its mathematical nature based on predictions and statistics. You will have experienced it yourself while using ChatGPT or other tools that rarely present the same output even if given the same prompt. This is a potential drawback in Agent Engineering because you will not have always the certainty getting the same results - which in some cases may not be a problem (think copywriting) or in others it could be (think medical cases).

To run such activities, we need guardrails. One of which is the Agent Engineering Framework outlined below, provides an idea for a structured approach to engineering agents:

1. Purpose: Define the core role (e.g., assisting in rare disease diagnosis)

2. Actions: Identify specific tasks (e.g., analyzing symptoms, reviewing medical literature)

3. Capabilities: Determine required skills (e.g., natural language processing, image recognition)

4. Proficiency: Set clear performance benchmarks

5. Technology: Select appropriate tools and technologies

6. Orchestration: Integrate all components into a cohesive system

Enabling Technologies:

• Large Language Models (LLMs): Foundation for natural language understanding and generation

• Retrieval-Augmented Generation (RAG): Access to external knowledge bases

• Function Calling: Interaction with external APIs and services

• Fine-tuning: Adaptation of pre-trained models to specific use cases

• Guardrails: Safety measures and ethical guidelines for responsible AI behavior

Breaking Down the Process: Step-by-Step Mapping

One of the most important pieces of this puzzle is to break down each single step of the process, instead of trying to achieve optimal results in one or ‘few’ shots. This can be done by using a simple map and ensuring some key questions are asked. Here's how to approach this systematically:

1. Define Objectives Clearly

   • What are the specific goals? Outline what success looks like for the agent.

   • Who are the stakeholders? Identify users, developers, and other impacted parties.

   • What constraints exist? Consider ethical, legal, and technical limitations.

2. Design the Agent’s Workflow

   • Input Processing: How will the agent receive and interpret data?

   • Decision-Making Logic: What algorithms or models will guide actions?

   • Output Generation: How will the agent communicate results or take actions?

3. Implement Modular Components

   • Data Ingestion Modules: For collecting and preprocessing data.

   • Core Processing Units: Where the main intelligence resides.

   • Interface Layers: Ensuring smooth interaction with other systems or users.

4. Establish Feedback Loops

   • Performance Monitoring: Continuously track the agent’s effectiveness.

   • User Feedback Integration: Incorporate user inputs to refine behavior.

   • Automated Self-Improvement: Enable the agent to adapt based on outcomes.

5. Ensure Robust Testing and Validation

   • Simulations: Test the agent in controlled environments.

   • Real-World Trials: Deploy in limited settings to gather authentic data.

   • Iterative Refinement: Use insights from testing to enhance functionality.

6. Deploy and Maintain

   • Scalable Deployment: Ensure the agent can handle increasing demands.

   • Ongoing Maintenance: Regular updates and patches to keep the agent current.

   • Security Measures: Protect against vulnerabilities and ensure data privacy.

(This is a longer article, if you prefer you can read it in a browser here.)

Feedback Mechanisms as Guardrails

Feedback mechanisms are another important type of guardrail: we need to develop some sort of feedback loop where explicit or implicit feedback is given to the process/agent. This way, we can improve the input and outputs over time. You can do this by implementing steps in the process that prompt scoring or evaluation of single tasks/outputs, and that in turn allows fine-tuning the data/training set or instructions for the agents.

🧠 Key Applications of AI Agents

1. Brainstorming and Ideation 💡

While traditional chatbots like ChatGPT assist with brainstorming by generating ideas upon request, AI agents take this a step further by autonomously managing and enhancing the ideation process. These agents are designed to integrate seamlessly into your workflows, proactively generate ideas, and collaborate with other tools and team members to facilitate comprehensive and dynamic brainstorming sessions.

How AI Agents Enhance Brainstorming and Ideation

Autonomous Idea Generation and Management

AI agents can autonomously generate, organize, and prioritize ideas based on predefined criteria and ongoing project requirements. For instance, an AI brainstorming agent can continuously monitor market trends, competitor activities, and internal project updates to suggest relevant and timely ideas without prompting.

Integration with Tools and Data Sources

Unlike standalone chatbots, AI agents can integrate with various tools and data sources to enrich the brainstorming process. They can pull data from project management software, CRM systems, market research databases, and even social media platforms to provide a comprehensive foundation for idea generation.

Proactive Collaboration and Facilitation

AI agents can facilitate collaboration by scheduling brainstorming sessions, inviting relevant stakeholders, and ensuring that all necessary materials and data are prepared in advance. They can also track the progress of ideas from conception to implementation, sending reminders and updates to keep the team aligned.

Continuous Learning and Adaptation

AI agents learn from each brainstorming session, adapting their suggestions based on past successes and feedback. This continuous learning process ensures that the agent becomes increasingly effective at generating valuable and innovative ideas tailored to your specific needs and preferences.

Examples of AI Agents in Brainstorming and Ideation

Marketing Strategist Agent

Imagine having a dedicated AI Marketing Strategist Agent that autonomously manages the ideation process for your marketing campaigns:

• Data Analysis: The agent analyzes current market trends, customer feedback, and competitor strategies to identify opportunities and gaps.

• Idea Generation: Based on the analysis, it generates a list of creative campaign ideas tailored to your target audience.

• Collaboration: The agent schedules brainstorming meetings, shares the generated ideas with the team, and facilitates discussions to refine and select the best concepts.

• Implementation Tracking: Once an idea is approved, the agent integrates with your project management tool to assign tasks, set deadlines, and monitor progress, ensuring seamless execution.

Product Development Agent

A Product Development Agent can revolutionize how new products are conceived and developed:

• Market Research Integration: Continuously gathers and analyzes data from market research reports, customer surveys, and industry news to inform product ideas.

• Idea Refinement: Suggests enhancements to initial ideas based on feasibility studies, cost analysis, and potential ROI.

• Prototype Coordination: Coordinates with design and engineering teams to develop prototypes, schedules testing phases, and collects feedback for iterative improvement.

• Innovation Tracking: Maintains a repository of all product ideas, tracking their development stages and outcomes to inform future ideation processes.

Content Creation Agent

For content creators, an AI Content Creation Agent can streamline the ideation and production process:

• Topic Discovery: Identifies trending topics and keywords relevant to your audience by analyzing social media, search engine data, and industry publications.

• Content Suggestions: Proposes content formats (blogs, videos, infographics) and specific topics tailored to your brand’s voice and audience interests.

• Workflow Integration: Integrates with your content management system to schedule posts, assign tasks to team members, and track engagement metrics.

• Performance Analysis: Analyzes the performance of published content, providing insights and suggestions for future content strategies to maximize impact and reach.

Real-World Implementation: Building Your Own Brainstorming Agent

For example, to develop an effective brainstorming AI agent, begin by clearly defining its objectives and scope, such as generating and managing creative ideas for digital marketing campaigns. Integrate essential data sources like CRM systems, social media analytics, and market research databases to provide comprehensive insights. Configure the agent to autonomously monitor data, generate ideas based on specific triggers, and facilitate brainstorming sessions by scheduling meetings and compiling minutes. Implement feedback loops to allow team members to refine the agent’s suggestions and prioritize high-impact ideas based on performance metrics. Finally, deploy the agent within your team, continuously monitor its performance, and make iterative adjustments to enhance its effectiveness.

2. Task automation and productivity 🚀

This could be applied to any type of task, so the beauty of it is that it can be 100% personal and unique.

This video is just an example of how people (very young people..) are starting to think about their tasks:

3. Practicing communication skills 🗣️

AI agents can serve as tireless conversation partners, helping users improve their communication skills in various contexts.

I've seen team members use AI agents to practice difficult conversations, like performance reviews or client negotiations. These simulations provide a safe space to refine communication strategies and build confidence. So an idea could be to build your own ‘AI coworkers’ (or Manager) and practice critical conversations/presentations, but have a workflow incorporate that feedback into your daily emails, or other presentations too.

4. Skill acquisition and learning 📚

Personalized AI tutors can revolutionize the learning process by providing tailored explanations, adaptive curricula, and instant feedback.

A recent good example I’ve seen is Shepherd.study, which combines AI-enabled self-study, affordable tutoring, peer collaboration. However this is still an AI ‘tool’, so the idea would be that you’d be able to build an agent of your own that also can prompt you to follow a learning schedule for example, or remind you to review key concepts at spaced intervals, go search for information, news, trends and updates.

🚀 Getting Started with Agent Engineering

So a lot of ideas, but gotta start somewhere right? Here’s a quick list of tools and steps to get started.

1. Popular platforms and tools for agent creation 🔧

Several platforms make it easy to get started with Agent Engineering:

• No-code platforms: Tools like Zapier and Make are the best out there, easy to use and with AI helpers to build workflows / agents.

• Advanced frameworks: For those with programming experience, libraries like LangChain, AutoGPT, or GPT-Engineer provide powerful tools for creating sophisticated AI agents.

• AI-powered development tools: GitHub Copilot, Replit, and OpenAI's GPT-4 API can be leveraged to assist in creating custom agents.

• Specialized agent platforms: Anthropic's Constitutional AI, AI Dungeon's custom AI models, and Hugging Face's model hub offer unique capabilities for agent creation.

2. Best practices for designing effective agents 📐

When creating AI agents, keep these principles in mind:

• Define clear objectives: Know exactly what you want your agent to achieve.

• Start simple: Begin with a focused task and expand capabilities gradually.

• Incorporate feedback loops: Regular use and refinement are key to improving your agent's performance.

• Respect AI limitations: Understand what AI can and cannot do, and design your agents accordingly.

🔮 TLDR;

Personally, I’m starting to build my own little ‘army’ of agents, that have specific tasks and goals for different projects in my personal and work life. Results vary highly, but most of all I find that this type of analysis (can I automate that? do I need AI for that or not?) helps me do some interesting thinking and develop new applications that I hadn’t thought of before.

Experimenting is training an important muscle, and exploring trends and what other people are doing is inspirational.

When do you plan to retire?

Typical answers range from ‘when I have enough cash’, ‘never’, or ‘when I’m 64 🎸’…

The average 20th-century worker studied for 20 years, worked for 45 years, retired at 65, then died 14 years later.

An industrial mindset told us when we could learn, earn and when we had to stop.

Today, the average life expectancy of women in France, Italy and Japan is past 86 years.

Babies born today in richer countries are expected to live past 100 years old.

Work is dissolving from steady jobs into platforms, shorter contracts, AI bots.

As we rethink the very concept of work, we need to consider how we pay for those who no longer work.

This article argues that the 20^th-century concept of Retirement should be Retired, it outlines alternative mental models for individuals around financial freedom, with some examples from countries and employers around the World.

What Have The Romans EVER Done for Us?

After 20 years of military service, in the Roman Empire a soldier was rewarded with citizenship and 10-13 years of pay. Another idea that started in ancient Italy and re-imagined by industrialisation.  Otto von Bismark introduced Germany’s first pension service in 1889, offering support to those over 70. Retirement was defined as a life stage, and governments saw the need to support aging populations, with the U.S. launching Social Security 1935.

However fast forward a century or so and we have some tension with pensions.

Pension Tensions - A System Under Pressure

The traditional three-legged stool of retirement - state pensions, occupational pensions, and personal savings - is wobbling.

Consider the warning signals :

We are living for longer, but retiring at a similar age. It’s great news that we are living longer healthier lives, as the graph below illustrates.

However the average retirement age has barely shifted over the past 50 years, hovering around 65 to 68. In the 1970s, a retiree might have only 10 years of retirement to plan for, in the 2020s, this is nearly twice as long.

The Roman pensions were so costly in the end that they accounted for up to 80% of state spending, forcing emperors like Augustus to implement inheritance taxes and extend service time to keep up with the expense.  Richer countries today can just about pay for pensions, but the cost is soaring. For example, the UK State Pension requires £200k in equivalent savings to provide a basic income, for a median salary of £35K (Source IFS).  As Merryn Somerset Webb points out state pensions might be means-tested in the future, based on individual needs. In her view, there aren’t many things that could bring the mild mannered British middle classes on to the streets - but not getting what they think they paid for pension-wise is one of them.    

The Job is Deconstructing. Younger generations are less willing to work for a boss and be mandated to work in a soulless office.  40% of Gen Xers plan to work part-time through retirement. Nearly half of Generation X doesn't believe they'll have enough saved for a comfortable retirement. 1 in 7 Americans have worked in the gig economy – without pension contributions. Less quality jobs means less pension contributions.

It’s a waste of Skills and Talent.  As the population ages, 70 is the new 60. We are healthier and better able to work than the last generation – due to medical advances. Forcing skilled and talented individuals to stop work at an arbitrary age is a waste.

Financial inequality. 2 billion people in the global workforce work informally without basic legal rights, let alone private pension provision. Globally, only 68% of retirement-age individuals receive any form of pension.

Disparity between Men and Women. Women need to work for an extra 19 years to retire with same pensions savings as a man in the UK, according to Pensions Policy Institute. In the United States, in the second quarter of 2021, women over age 65 had median weekly earnings that were 72% of men's, earning $816 compared to $1,141.

Retirement Shock. In the 1970s, British postal workers had some of the shortest retirement spans, 7 or 8 years on average. For teachers this was 12-15 years. Things have improved today, but reasons in studies at the time gave physical demands of long hours on foot for posties. There is also the less dramatic retirement shock to the spouse of having to share their home with their partner during the day time.

So the pension and retirement concept is wobbling,

These are long-term structural issues with financial security, pension and retirement that will probably take decades to work out, so what can we do as individuals? 

This is a longer article - here is a link to read “Retiring Retirement” in a browser.

Taking Charge: Managing Your Career and Finances

I think we need to rethink our conceptions of  Work, Leisure, and Wealth.

We need to strive for financial independence in a way that suits our needs, and of course take more personal control of our careers and finances than in the 20th Century.

Strive for Financial Independence. Whether you are 20, 40 or 60.  Start saving up for a fund that pays your expected expenses when you stop getting paid for work. (see below for some ideas on this). Pensions are a rather rigid way to provide a regular income stream when your work income stops. The pensions industry is full of poor advice, vested interests and somebody willing to take 1.5% of your savings at every stage in the process.

Take control of your work and finances. You should decide when you want to stop paid work, not the state or employer.  We can’t assume our state or employer will look after us in the same way today in a few decades time. If 50% of your financial security is in an employers’ investment fund – then make sure you know about the fees you are paying and how the fund is performing against the benchmarks. Understand the rules in your country to make sure you receive the state pension. Take an interest in personal finance and find a trusted financial advisor. (I have included some useful resources at the end of this article)

Do work that gives you fulfilment and aligns with your values. This has always been the goal hasn’t it? You will not want to stop. Paid, unpaid, recognised, valued - it doesn’t matter. The solution to the retirement problem for individuals is to do work you never want to retire from. I will continue to write about building a better future of work until you stop reading my articles or stop coming to my talks!

Stay healthy. There is no point in working relentlessly for 30 years to save for your Lifetime Expenses Fund, but then spend the rest of your life lonely and battling chronic avoidable lifestyle diseases. That would be very 20^th Century - but arguably better than a Roman soldier, where only 50% even made it to retirement.

21^st Century Careers aren’t like your fathers’ careers. We will be constantly working on projects, caring, convalescing and earning and learning at different paces. Instead of 1 or 2 careers, we will be having 100 different careers and 100 different lives, or as Matteo refers to - many lateral careers.

As you master work and learn more about your financial tactics for you and your family, I suspect your notion of retirement will change.

This is my personal perspective, all these are easier to say than do, and very much depend on your context, where you live etc

Please share your thoughts in the comments.

Come on Baby Light my FIRE🔥

When Will You Stop Working?

In our poll in an earlier article, (which is still open) 73% of readers indicated that they will stop working when they have enough cash saved.  Financial security is an important aspect of when to stop working.

There are a group of individuals who are deeply into the concept of Financial Independence. A quick scan on the Reddit groups shows this is quite a diverse group of individuals, from the Google Executive, able to save 50% of a substantial income, to the young family just about breaking-even every month.

FIRE stands for Financial Independence and Early Retirement

The core philosophy is about taking control of your financial life to gain the freedom to pursue your passions without being constrained by traditional employment.

Another example,

The aim is to reach a point where your investments generate enough passive income to cover your living expenses. This financial independence allows you to retire early or choose work that's meaningful to you, free from financial pressures.

The underlying Philosophy tends to push

• Empowerment - Taking responsibility for your financial future empowers you to live life on your own terms.

• Intentionality - Being deliberate about your choices leads to greater satisfaction and less waste. We can all make smarter choices even though we all have very different options.

• Freedom over Luxury - Valuing time and freedom more than accumulating material wealth.

This way of thinking challenges conventional notions of success and retirement, proposing that with discipline and strategic planning, one can achieve a fulfilling life sooner rather than later.

Key Principles for achieving FIRE include :-

1. Living below your means: to save a significant portion of your income, accelerating wealth accumulation.

2. Smart Investing: Putting your savings to work through investments in stocks, bonds, real estate, or other assets that appreciate over time.

3. Frugal Living: Making conscious choices to reduce unnecessary expenses, focusing on value rather than consumption.

4. Mindset Shift: Changing how you view money and success. Instead of equating happiness with material possessions, the focus is on financial freedom and personal fulfillment.

5. Life Design: Actively shaping your life to align with your values and goals, which may include downsizing, simplifying, or pursuing alternative career paths.

6. Continuous Learning: Educating yourself about personal finance, investing, and self-improvement to make informed decisions.

I have put some useful FIRE resources at the end of this post.

How Much Do you Need to Stop Working?

This is the million dollar question for most people.

If you retire at 66, and want a median lifestyle, you will need a fund to support you.

Here is a crude summary of the additional fund you will need in selected countries.

To determine how much cash you’ll need to stop paid work in a future scenario, we break it down into key steps :-

• determine your goals and lifestyle - location, travel etc.

• estimate annual expenses including housing, food, healthcare etc

• assess sources of income from state and private pensions, savings, investments

• multiply by 25 to set an adjusted target (this is the the 4% rule – ask an actuary if you dare). Example if your estimated annual expenses are $50k, you will need $1.25m.

Now you have your Lifetime Expenses Fund - get saving, spending less and planning your future.

So how are countries and companies responding to the ageing workforce?

How Countries and Companies Are Responding

Here are some selected example from around the World on how countries and employers are responding to the aging workforce.

Increase the retirement age - with people living twice as long after retirement than the 1970s - this is the obvious response. In China the retirement age is set as 63, for women it varies from 55 for blue-collar workers to 58 for white-collar workers. Many others have retirement between 64 and 66.

Sweden has led Europe in offering flexible retirement options - Swedes can choose to retire between ages 62 and 68, and many choose to work part-time, collecting partial pensions while staying engaged in the workforce. Germany also has the ‘Altersteilzeit’ program, a form of phased retirement for older workers that allows them to transition gradually.

Intergenerational Knowledge Transfer and Training Programs - in France, companies like Airbus and Renault have set up ‘intergenerational contracts’ offering financial incentives to older employees to train younger workers before they exit the workforce.

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Conclusion

The 20th-century model of retirement is becoming as obsolete as the Roman military pension system. As we face the prospect of 100-year lives, we need new paradigms that support meaningful, sustainable ways of living and working across all life stages.

The concept of retirement is creaking as the populations ages, more work in the gig economy, and industries get transformed by the digital revolution.

There are many sensible tactics for individuals to design their lives, their work, their finances.

Employers and countries are adapting far too slowly, with serious repercussions.

Finally DYOR - Do Your Own Research.  Nothing in this article constitutes financial advice🤦🏼. Every person has a different context and there are no black and white answers here.

The social solutions are not to abandon retirement, but to reimagine it for the 21st century.

Further Resources

• Retire in Progress - some smoking hot 🔥 FIRE thinking and resources from Mr Rip.

• AARP Retirement Planning Tools (US) - The American Association of Retired Persons (AARP) offers calculators and resources for retirement planning.

• National Insurance Contribution Credits (UK) - make sure you don’t miss out on crucial pension contributions to be eligible for the full state pension.

• Pension Wise (UK) - A government service offering free, impartial guidance on defined contribution pensions.

• HerMoney - by Jean Chatzky - articles, podcasts and webinars on money management and retirement.

• Clever Girl Finance - by Bola Sokunbi - valuable tips on investing, free courses, budgeting, and careers.

• Redirecting Work - by Lexy Martin - lots of interesting personal stories and thinking, find out about Cocooning and check out the interviews with individuals working in Work Tech and Future of Work.

• Ellevest (US) - designed specifically for women, Ellevest provides investment and retirement planning services, educational content, and financial coaching.

Awaiting your 50-year life plans,

Andy