Work3 - The Future of Work

35 New AI Roles to watch out for

Matteo Cellini — Wed, 08 Jan 2025 09:00:00 +0000

Welcome to this wonderful new year with Work3!

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 hypothesis, 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 kind of role pretty 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 Ethics Consultant: This new role addresses the unique ethical challenges posed by AI. Unlike traditional ethicists, they must grapple with issues like AI bias, the impact of AI decisions on human lives, and the long-term societal effects of AI adoption. An AI Ethics Consultant might help a company ensure their AI hiring tool doesn’t perpetuate existing biases, a concern that didn’t exist with traditional hiring methods.
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 AI Ethics Officer: Focuses exclusively on the ethical implications of AI use across an organization. Unlike traditional ethics officers, they must grapple with rapidly evolving AI capabilities and their potential impacts. They might develop company-wide policies for responsible AI use, oversee the implementation of explainable AI systems, and ensure that AI adoption aligns with the company’s values and societal expectations.
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 mentioned this in our webinar/article on Mega Trends with Andy recently, and stand on 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.

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

Help me Improve Work3

35 New AI Roles to watch out for

Matteo Cellini — Wed, 08 Jan 2025 09:00:00 +0000

Welcome to this wonderful new year with Work3!

Let’s dive in!

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

1. AI Specialized Roles

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 kind of role pretty 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.

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

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 mentioned this in our webinar/article on Mega Trends with Andy recently, and stand on 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)

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

Lots more to come on this topic!

Ciao, Matteo

Help me Improve Work3

Five Reasons To Be Optimistic About Work in 2025

Andy Spence — Wed, 01 Jan 2025 09:00:00 +0000

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.

From the Manpower Group Employment Outlook Survey Q1 2025

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.

I know you’re out there…I can feel you now. Neo, The Matrix

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

Source - InflationData.com

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.

Money can’t buy you happiness but it does bring you a more pleasant form of misery.

Spike Milligan

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

No commute
Save on travel and lunch costs
Flexibility over where I work
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

…build the audience first, then test and develop…

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.

You are reading Workforce Futurist Newsletter - news, views and insights on the future of work going to over 26,000 every week. If you haven’t already, please consider subscribing (it’s free), and sharing with those in your network who might appreciate it.

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.

I don't know the future...I didn't come here to tell you how this is going to end, I came here to tell you how this is going to begin. Neo, The Matrix

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

Help me Improve Work3

The Best of 2024: 10 Articles That Shaped Our Thinking About the Future of Work

Matteo Cellini — Thu, 26 Dec 2024 09:00:00 +0000

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

Stifle the groan folks, because this time it might really be different….this mind bending post on the ramification of AI-enabled candidates meeting AI-enabled recruiters and how this might produce disparate outcomes such as idiosyncratic candidate profiles generated on the fly as candidates apply, and permanent talent passports which can be ported from one walled city to another. Great read. CV will indeed be dead, but they will still be around, albeit in zombiefied state.

Hung Lee in Recruiting Brainfood.

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.

I love blissful optimism, but only in the movies: in the real world it doesn’t work.

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

Turns out we can’t predict the future, but we can predict your prediction.

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.

5 - Retiring Retirement

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

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.

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

Not only do I like this for all the emotive allure of equity, pace, inclusion, creativity and meaningful work, it doesn't even sound outlandish and utopian. Perry Timms.

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

5 Big Questions for Work in 2025

Wed, 18 Dec 2024 08:58:12 +0000

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.

Thanks for reading Workforce Futurist which goes out to over 25,000 Subscribers every week. If you haven’t done so already, please subscribe for free. We have lots planned for 2025!

Subscribed

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?

We’ve decided that we’re going to return to being in the office the way we were before the onset of Covid. This will help staff be better set up to invent, collaborate, and be connected enough to each other. CEO Amazon

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:

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.
Think like a business, $me myself.Ltd and I.Corp - develop your personal brand, develop a diverse sales pipeline, assess supply and demand etc
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.
Stay healthy mentally and physically - obviously, but don’t burnout because of your work.
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.
Commit to Lifelong learning - no school, mentor, university, employer will teach you what you need over a long life. Stay curious.
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

Help me Improve Work3

Building a Better World Without Jobs

Wed, 11 Dec 2024 09:00:00 +0000

“the goal of the future is full unemployment, so we can play.” Arthur C. Clarke

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…

Thanks for reading Workforce Futurist which goes out to over 25,000 Subscribers every week. If you haven’t done so already, please subscribe for free. This will help us deliver more in 2025.

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.

Source :- Max Roser - Our World In Data

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%.

Graph by Branko Milanovic/Christoph Lakner; elephant added by Caroline Freund

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”.

“We need new mechanisms for distributing abundant output in a world which labour is made economically redundant by autonomous machines” Anton Korinek and Megan Juelfs

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.

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,

“Education will become the largest single industry and entertainment a close second-or mankind would die of utter boredom in a workless world.”

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 for our FREE event Here!

Help me Improve Work3

Future of Work in 2024 – Latest Scores

Andy Spence — Wed, 04 Dec 2024 09:00:00 +0000

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…

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

Help me Improve Work3

Beyond Prediction: Shaping Your Work Story in an Uncertain World

Andy Spence — Wed, 27 Nov 2024 09:00:00 +0000

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.

According to Christopher Booker, there are seven basic plots that underpin almost every novel, movie, or even TV advert we encounter. Imagine your career as one of these archetypes:

Are you on a quest for purpose, overcoming challenges, or writing a rags-to-riches story?

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 is 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 personalized 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 first?

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 [add link to my critique]. 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 will we stop tying our identities to work? This is the utopian narrative I want to believe in, but the practicalities are daunting. Work deintensification would require significant cultural shifts and systemic reforms, which 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?

Dataism: Algorithms dominate decision-making, sidelining human intuition.
Exterminism: Robots and AI lead to catastrophic job losses.
Re/Upskilling: Lifelong learning is necessary to remain employable.
Augmentation: Humans and machines collaborate in harmony.
The Singularity: AI surpasses human intelligence, reshaping society.
Job Destruction: Automation erodes low-skilled jobs, deepening inequality.
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 manty fear-based narratives.

As I mentioned in my article 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

Help me Improve Work3

The End of Typing?

Matteo Cellini — Wed, 20 Nov 2024 09:00:00 +0000

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, 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

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.

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:

Hi, I’m Alec Beckett, creative partner at Nale Communications, a creative agency in Providence. Now, we have a client who’s been really hesitant to make subjective decisions until his CEO has weighed in. But of course, the CEO was always busy, and it’s very hard finding time on our calendar. So we were seeing timelines starting to slip when Stephen, our director of strategy, came to me with an idea that I really loved. Let’s make a synthetic version of the CEO. So we trained a custom GPT with the CEO’s latest strategic plan and as many of her speeches and blog posts and podcast transcripts as we can find. Then before we presented the next round of work to our client, we uploaded that presentation to our synthetic CEO and asked for an opinion. The feedback was actually kind of amazing. It wasn’t all positive. But frankly, it was exactly the kind of feedback we’d love to get more often from our clients. It’s very cogent and strategic and clear. And it was really interesting to see how that sort of loosened up our client who felt like he was getting a version of his CEO’s perspective. And it seemed to make him more willing to make some of these decisions and keep the process moving forward. It did get us start to wondering, like, are synthetic CEOs the future? I mean, they certainly are cheaper.

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:

At a mid-sized software company, they created what they call a "Voice Time Machine" - developers can have conversations with previous versions of their codebase. By training the AI on historical documentation, commit messages, and team discussions, developers can ask questions like "Why did we make this architectural decision last year?" or "What were the main concerns when we designed this feature?" This has dramatically reduced the time spent digging through old documentation and helped new team members get up to speed faster.

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.

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

Hire an AI BDR & Get Qualified Meetings On Autopilot

Outbound requires hours of manual work.

Hire Ava who automates your entire outbound demand generation process, including:

Intent-Driven Lead Discovery Across Dozens of Sources
High Quality Emails with Human-Level Personalization
Follow-Up Management
Email Deliverability Management

Hire Ava to slash costs & boost productivity.

Help me Improve Work3

The Next Wave of AI: Building Your Own Digital Workforce

Matteo Cellini — Wed, 13 Nov 2024 09:00:00 +0000

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 XYZ task 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 pardigm 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:

Purpose: Define the core role (e.g., assisting in rare disease diagnosis)
Actions: Identify specific tasks (e.g., analyzing symptoms, reviewing medical literature)
Capabilities: Determine required skills (e.g., natural language processing, image recognition)
Proficiency: Set clear performance benchmarks
Technology: Select appropriate tools and technologies
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:

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.
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?
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.
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.
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.
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.

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.

Here’s Why Over 4 Million Professionals Read Morning Brew

Business news explained in plain English
Straight facts, zero fluff, & plenty of puns
100% free

See for yourself

🚀 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.

Ciao,

Matteo

Help me Improve Work3

Retiring Retirement

Andy Spence — Wed, 06 Nov 2024 09:00:00 +0000

Livraria Ler Devagar, Lisbon. Photo by Andrew Spence

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.

Life Expectancy - from Our World in Data

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.

Life Expectancy at 65 - Japanese women have another 25 years on average - from OECD

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.

Gender pension gap in 2019 - Eurostat

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.

Tom and Lisa, a couple from the UK, embraced the FIRE mindset in their 40s despite modest incomes. They focused on ‘frugal living’ to save 30% of their combined salaries, cutting down on expenses like dining out and choosing budget-friendly vacations. Over 15 years, they invested in stocks and bonds, which provided them with a stable passive income stream. By 55, they’d reached financial independence and could both downshift their jobs, allowing them to spend more time on their hobbies and with family.

Another example,

Luca, a freelance web developer in his 20s, took a unique path to FIRE. He minimized his living expenses by moving to lower-cost countries like Thailand and Vietnam, where he could live comfortably while saving over 50% of his income. By investing in a diversified portfolio and keeping a minimalistic lifestyle, he achieved financial independence by 35. Now, Luca has the flexibility to work on freelance projects he’s passionate about, travel, and live on his terms.

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 :-

Living below your means: to save a significant portion of your income, accelerating wealth accumulation.
Smart Investing: Putting your savings to work through investments in stocks, bonds, real estate, or other assets that appreciate over time.
Frugal Living: Making conscious choices to reduce unnecessary expenses, focusing on value rather than consumption.
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.
Life Design: Actively shaping your life to align with your values and goals, which may include downsizing, simplifying, or pursuing alternative career paths.
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.

Looking for unbiased, fact-based news? Join 1440 today.

Upgrade your news intake with 1440! Dive into a daily newsletter trusted by millions for its comprehensive, 5-minute snapshot of the world's happenings. We navigate through over 100 sources to bring you fact-based news on politics, business, and culture—minus the bias and absolutely free.

Subscribe to 1440 today.

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.

Recommended retirement fund needed for a 66 year old - Sources UK, US, Sweden, France, India

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.

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

Help us Improve Work3

2034 – A Day in the Working Life

Andy Spence — Wed, 30 Oct 2024 09:00:00 +0000

Sit back, suspend disbelief, and join me in 2034….

The year is 2034, and I marvel at how different everything is from my parents’ working lives. 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. Just yesterday, I enrolled in a cutting-edge Disney-Harvard course on Virtual Architectures, joining brilliant minds from across the globe. A programmer from Soweto, an artist from Stockholm, a designer from Seoul, and a neuropharmacologist from São Paulo. Access to the course depends solely on our capability rather than privilege and our achievements are verified instantly. The pressure to keep learning can feel overwhelming but it's a far cry from the crushing student debt of the past, and the lottery of who teaches us.

The work landscape has evolved just as dramatically - gone are the rigid 9-to-5 schedules and 90 minute commutes in polluted traffic. My mornings flow between diverse projects: coding for climate initiatives, designing virtual concert spaces, and mentoring junior developers. Each contribution rewards me with Equity Tokens – bursts of digital ownership dropping into my account. The Work-Matching Platform suggests these projects based on my skills and interests, with uncanny accuracy. Not every project becomes the next Amazon-MIT, Uber-Volkswagen, or, Apple-Samsung, but the opportunity to collaborate is rewarding.

Last week, I collaborated with a team spread across five continents to solve a complex energy storage problem. We formed a temporary company, pooled our skills, and shared the rewards equally. No middlemen, no corporate hierarchy - just pure innovation and some success. What amazes me most is how seamless it all is. The platform handled everything - instantaneous business registration, smart contracts, tax calculations, equity distribution.

This new system is built on a foundation of fair contribution through the Public Fund. This fund pays for all the public goods that our society needs, from health to transport to carbon capture mechanisms. My platform automatically calculates payments based on revenue and AI usage – no more complicated tax codes. The more I leverage public AI infrastructure, the more I contribute back. Even my current climate project tracks its AI usage and productivity gains, calculating micro-payments in real-time. Moving to the Public Fund was a difficult transition for many Cities, but those that had access to AI capabilities and could articulate the benefits to their citizens flourished.

The human element hasn't been forgotten. My friend Myra exemplifies this – she receives a Basic Income while caring for her elderly mother, recognition that care work carries as much value as any other contribution.

I sometimes think back to what I learned about the history of work - from the factories to the cubicles to remote-work (now known as just work) in the 2020s. Reliable jobs, salaries, and pensions had served its purpose for many. Globalisation was a double-edged sword. Yes, it lifted hundred of millions out of poverty in China, and the Global South, but it also squeezed worker wages in the West. Technology was unbundling everything - whole industries, skill sets, the very notion of employment itself.

The real change came when politics finally caught up with reality. The platforms we use now are public utilities, governed by users like me. Sure, we still have disputes - we're human after all - but the Voting Systems for resolving them are elegant, fair, and swift. Talent-matching algorithms are open-source, transparent, and have gained our trust through years of success. I love how employers now focus purely on capability matches. My cousin, who struggles to speak due to a neurological condition, gets opportunities that would have been denied to her a decade ago. If the algorithm shows a 98% skills match, that's what matters.

The old digital landlords who hoarded job candidate data are gone. In their place, we have flourishing global communities of skilled professionals, forming and reforming like schools of fish, moving together toward whatever challenges need solving.

Being able to work so much more effectively helped us to solve some of the wicked problems that our societies had. We responded to crises - pandemics, hyper-inflation and adapting to global warming. We were working on solutions within hours, not months. Looking back, it seems obvious that we needed to rethink benefit distribution as machines generated more value.

Anyway, another day of learning, leisure, and work projects awaits tomorrow. Or am I dreaming?

Reporting from 2034,

Andy

Help me Improve Work3

Death of the CV

Matteo Cellini — Wed, 23 Oct 2024 08:00:00 +0000

A few years back, I wrote on how CVs were dead (and how DAOs and the blockchain would have taken over). This 500-year old piece of digital paper (invented by my fellow countryman Leonardo Da Vinci) has been the main vehicle for recruiters and hiring managers to screen candidates, based on a) the ‘pedigree’ or prestige of the companies and experience b) the capability of the candidate to actually showcase and storytell their skills. This may have worked in a much more linear job market; relatively small number of applicants, evident and sizeable differences. As it happens with all ‘markets’ (yes, remember that the job market is like any other - buyers and sellers in this case are people and their skills) globalization and competition started to put this method to the test. In the meantime, DAOs (decentralizied autonomous organizations) kind of faded in popularity with their unfortunate ‘dependance’ on the Crypto market (plus some structural problems like anonymity, and such) and AI took the world by storm.

So, is the CV dead - and what might replace it?

Let's dive into how Web3 and AI are reshaping the job market, from application processes to the very concept of professional identity.

1. 🤖 The Rise of AI in Job Applications

Thought that tailoring your CV for each application was the gold standard? Now AI has entered the chat, and it's changing everything…not always for the better.

AI-Powered Job Applications: The New Normal

According to the Microsoft Work Trend Index 2024, AI usage in the workplace has nearly doubled in just six months, with a staggering 75% of global workers now using AI tools. For job seekers, this translates into a new era of "personalized mass apply."

📊 AI-assisted applications are becoming ubiquitous
🚀 Job seekers can now apply to hundreds of positions with personalized applications in record time
🎯 The quality and relevance of applications have improved, thanks to AI's ability to tailor content to job descriptions

A few examples:

AI Apply - AI generated CV, cover letter, followup email and auto-applications

Simplify Jobs - All of the above, plus tracking for your job applications

The results? If one motivated job seeker could make 100 applications per week, AI tools now enable to submit thousands.

Andy also wrote a list of 20 AI tools to help in job search which you should definitely check out.

Are AI-fuelled applications a good thing?

💥 The job market is experiencing a "DDoS attack" of applications in some cases
🌊 Employers are overwhelmed by the flood of applications
⏱️ Job postings are becoming ephemeral, sometimes lasting mere hours before being taken down due to application overload

This story is also tied to an increasingly ‘cool’ job market, and averages of 200-400+ applications for each job. Pathrise mentions a 20% increase year on year, but that’s just a global average.

Just check out some dedicated sub-Reddits where job seekers post Sankey diagrams of their journey - monstrous numbers of required applications, lots of ‘ghosting’ and so on:

2. 🔍 Employers' Response: Fighting AI with AI

Faced with this deluge of AI-enhanced applications, employers are turning to AI themselves to manage the influx. But is it working?

AI-Powered CV Review and Candidate Summaries

🧠 AI is now better at reading CVs than humans, especially at scale
📈 Major ATS providers are integrating AI CV review as a standard feature
🔄 Employers are shifting from reading CVs to reviewing AI-generated candidate summaries

However, the claim that AI reads CVs better than humans overlooks crucial nuances in the hiring process. As one hiring manager points out in a dedicated thread:

"I still receive ALL applications submitted. I prioritize resumes with higher scores but usually end up reviewing every resume received." This human touch is essential because, as another recruiter notes, "If you have the type of experience and skills needed for the job and have a professional looking resume with little to no spelling and grammar errors, I'll at least have a recruiter contact you for more information."

Furthermore, the increasing use of AI in applications has led to new challenges. One HR professional observes, "Now that AI is in the picture, the candidates who previously were too lazy or not skilled enough to write a compelling resume and cover letter are using AI to do so--which means now NO well constructed resumes and cover letters stand out."

The Privacy Paradox & Contrasting views on AI

Every time I sent my CV in my past job searches, I felt like I was giving away two things: privacy, and my data.

According to a Pew Research Center study:

🧠 Awareness gap: 61% of Americans have heard nothing at all about AI being used by employers in the hiring process. This is huge.
👎 Opposition to AI: 41% oppose employers using AI to review job applications, while only 28% favor it. For final hiring decisions, 71% oppose using AI.
🤖 vs 👤 Human touch: The most common reason (44%) for not wanting to apply is concern that AI would miss the "human factor" in evaluating candidates.
🎯 Potential for fairness: 47% believe AI would do a better job than humans at treating all applicants in the same way.
🚫 Reluctance to apply: 66% of Americans say they would not want to apply for a job where AI is used to help make hiring decisions.
🏳️‍🌈 Bias concerns: 79% of Americans say bias and unfair treatment based on race or ethnicity is a problem in hiring. Among those who see it as a problem, 53% believe increased use of AI in hiring would help reduce this bias.

It's interesting to see how there are contrasting opinions and views on how AI could be removing some human biases, whilst on the other hand there is reluctance to feel evaluated by a machine. This paradox highlights the complex relationship between technology and human decision-making in the hiring process. And we're just at the beginning…

3. 🌐 The Future of Professional Profiles

So, as AI becomes more prevalent in hiring, we're seeing/needing the emergence of new forms of professional identity verification. This shift is driven by the need for more reliable, comprehensive, and AI-compatible ways to represent professional credentials and experiences.

Here's how this is unfolding:

1. 🏰 "Walled Cities" with Idiosyncratic Records

Platforms like LinkedIn, Indeed, and niche job boards are becoming "walled cities" with their own verification processes.These platforms are developing unique ways to verify that users are human and establish their professional credibility. Internal reputation systems (think karma scores) are becoming as important as traditional qualifications.

2. 🔗 Blockchain-Based "Talent Passports"

Blockchain technology offers transparency, validity, and identity verification in the hiring space. "Talent passports" could allow professionals to carry verified credentials across platforms. These passports might become the new standard for establishing professional identity in a world where traditional CVs are no longer trusted. Check out the interview with Velocity Labs’ founder I did a while ago. (Web3: The Internet of Careers)

An example is Talent Protocol, who have a system for managing professional reputation online. They are calling it a "passport to the onchain era of the internet". This means it uses blockchain technology to store information about your skills and experience in a secure and verifiable way.

Some of the key features:

Talent Passports: These are like online resumes that store verifiable information about your skills and experience.
Builder Score: This is a score that Talent Protocol calculates based on the information in your Talent Passport. It is designed to help people find each other with similar skills and interests.
$TAL Token: This is a token that will be used to pay for access to Talent Protocol's features.

3. 🧬 Comprehensive Skill Profiles

AI-powered skill assessments are creating more dynamic and detailed skill profiles. Continuous learning tracking platforms monitor and verify ongoing professional development.

Examples:

Degreed - One of the main platforms that helps enterprises track, manage, and verify educational credentials. It allows users to create a digital portfolio of their skills and experiences, making it easier for them to showcase their qualifications to potential employers or educational institutions.
Runn.io, iMocha - Smaller platforms purely focused on the skills piece

4. 🤝 Enhanced Reputation Systems + Proof of Work

Peer endorsements are evolving beyond simple recommendations to include more weight and verification processes. Project-based portfolios are showcasing and verifying completed work, potentially replacing traditional resumes for certain roles (think of Behance for designers - a platform where they showcase their work). In many cases this is not applicable, so ‘proof of work’ (another blockchain-based idea) may turn into ‘work trials’ (essentially short programs where candidates actually do a real-life business case, or become part of the company for a time-boxed period) but there’s a lot of controversy with regards to their efficacy.

Last, but not least - some companies are trying to rethink referrals from the ground up, like getCredible for example - an app that looks to get anonymous ratings in your network in exchange for a referral fee if the person gets hired.

5. 🧠 Skill Profiling

AI-driven personality assessments analyze communication patterns and other data to create comprehensive profiles, and virtual reality assessments test soft skills and situational judgment in simulated work scenarios. However, the most ‘real’ type of skill profiling we have today is that one by companies like TestGorilla, who get ‘experts’ to compile time based tests that should verify skills instead of judgment based on the CV.

The CV is not Dead - It’s a matter of Time

With all these changes, we have to ask: Is it still a CV if a human didn't write it, and a human isn't reading it?

Traditional CVs will continue to exist but will lose their central role in hiring
AI-generated and AI-read documents are fundamentally different from traditional CVs
The focus is shifting from standardization to interoperability of professional records

Changes like this one always take time, but AI may have sped up the CV’s death by accelerating the amount of spam.

Writer RAG tool: build production-ready RAG apps in minutes

Writer RAG Tool: build production-ready RAG apps in minutes with simple API calls.
Knowledge Graph integration for intelligent data retrieval and AI-powered interactions.
Streamlined full-stack platform eliminates complex setups for scalable, accurate AI workflows.

Learn more about our production ready RAG tooling here.

The new game for Job Seekers:

Focus on building a strong, verifiable online presence across multiple platforms
Develop AI literacy to effectively use these tools in your job search
Don't neglect human networking—authentic connections will become even more valuable

For Employers:

Invest in AI-powered hiring tools to manage the increased volume of applications
Develop new methods for assessing soft skills and cultural fit that AI might miss
HR - develop and nurture alternative pools of future talent
Stay informed about the legal and ethical implications of AI in hiring

The Human Element in an AI World

In this brave new world, our professional identity will be more than a document. It will be a living, verified, and constantly updating digital representation of our skills, experiences, and potential. That said, the human element will become more important - but not because we like to think of it being ‘important’, more simply because it will be the only way to cut through the huge amounts of noise that is hitting the job market like an avalanche.

One of the Five Pathways we mentioned in our article towards a possible Golden Age of Work, was “Decentralised work infrastructure for effective work-matching”. With searchable, verifiable CVs no longer trapped in “Walled Cities”, we open up a world where we get much more efficient work-matching. Total disruption of the current recruitment industry as it stands - who not only own the Walled Cities - but charge us entry to pay a visit!

With our new verifiable professional wallets, we might speculate that this will enable large-scale work-matching platforms. Think Upwork, but with lower matching fees and more verification. Swathes of skill-sets will not need recruiters at all. For employers, making it easier and cheaper to hire, will mean we will not need to have thousands of employees on our books.

So will the death of the CV lead to the death of the traditional firm as we know it?

Help me Improve Work3

Will Digital Workers Revolutionise Work?

Matteo Cellini — Wed, 16 Oct 2024 08:00:00 +0000

AI is not going to be the real game-changer for the future of work.

It's going to be AI + workflow automations, defined as 'AI Agents' (and in the long term could become AGI, Artificial General Intelligence).

That's why today we're covering a start-up that is playing in this field, 11x.ai, founded in 2022 by Hassan Sukkar.

I've had a chat with Harry Bryars, Chief of Staff and one of the first employees who's been through the process from the beginning. We covered the company but also his thoughts on the future of work

Company Background

Since launch, they have experienced remarkable growth and secured significant funding including the most recent $24 million Series A funding round led by Benchmark.

At its core, 11x.ai focuses on creating AI digital workers – sophisticated AI systems designed to autonomously handle various tasks and processes within an organization. Their mission: simplify and optimize go-to-market operations for businesses of all sizes. They believe that within the next two years, digital workers will become an integral part of how companies operate globally.

Core Products: AI Digital Workers

1. Alice: AI SDR

Alice is an AI-powered Sales Development Representative (SDR) designed to handle the entire outbound sales development process. Her (?) capabilities include finding relevant prospects, researching potential clients, writing personalized messages, sending emails, following up, and booking meetings.

What makes Alice stand out is her ability to adapt and improve continually. One of the key enhancements has been in matching human writing styles and tones. As Bryars notes, "We've just begun but made tons of improvements, and over time we've gone through an evolution where people aren't detecting that this is AI". And it's not just the words, it's also the information. "It's having the right information, accurate information, referencing it in a way that feels natural."

2. Jordan: AI Caller

Jordan, the newer addition, is an AI-powered caller capable of handling both inbound calls and consented outbound calls. While still in its early stages, Jordan represents a significant step towards creating a comprehensive AI-driven GTM team.

One of the most exciting aspects of Jordan is its integration with Alice. Bryars highlights this synergy: "Jordan can handle a phone call and then hand over to Alice to make it, you know, follow up via email or the other way around. So they'll always be super integrated."

This integration showcases their vision of creating not just individual AI employees, but an entire AI team capable of handling complex, multi-channel GTM processes. A vision which also OpenAI seems to be going after, as they recently announced Swarm, a multi-agent orchestration framework.

Target Market and Use Cases

I asked Bryars who their main customers were, curious about the type of companies looking to experiment with this kind of solutions. While initially they started by serving a wide range of customers, including small startups, they have since refined their focus. "Over time, we've definitely moved up-market. Most of our customers now are mid-market and enterprise."

This shift in focus stems from the realization that 11x.ai's digital workers are best suited for companies with established sales processes and product-market fit. As Bryars puts it, "We help you scale your existing performance. We don't help you find success where there isn't success necessarily."

We went through the core use cases for their digital workers:

1. Scaling Existing Teams: Some companies use AI workers to augment their human SDRs, allowing them to handle a larger volume of prospects and communications.

2. Replacing Human SDRs: Other businesses have used Alice to take over the role of human SDRs entirely, freeing up those employees to move into more advanced roles such as junior Account Executives or AEs.

3. Reducing Hiring Needs: In some cases, companies have significantly reduced their planned hiring. Bryars mentions, "We have some customers where they planned to hire, I don't know, like a dozen people, and now they only need to hire one person."

4. Empowering Account Executives: Some customers have AEs using Alice directly to generate their own pipeline, reducing the need for a separate SDR team.

While all of them are interesting, they will all come with their own problems.

Impact on Workforce

The introduction of AI digital workers like Alice and Jordan is going to change the landscape of sales and GTM operations. However, the impact on the workforce is not as straightforward as simple replacement.

Bryars emphasizes that for most of their customers, especially in the mid-market and enterprise segments, the goal is growth rather than cost-cutting. "Most of our customers, especially given the segment that we're targeting, are looking to grow as quickly as possible. And they know that doesn't just come from removing people out of the equation. It comes from shifting their focus to be more impactful," he explains.

This shift often involves moving human workers away from repetitive, time-consuming tasks and towards more strategic, high-value activities. As Bryars puts it, "Our ultimate vision is that we can unblock a bunch of that repetitive, low-level work and allow you to spend your time doing much more creative, impactful, resourceful things."

Reflecting on the broader implications of AI in the workforce, Bryars shares a personal perspective: "I think on the personal side, it's like the biggest question on my mind, not related to us, but is like, how will the world evolve? As an example, I've been taking Waymo a bunch in San Francisco. Waymo, the self-driving cars. They're incredible. But it's very interesting to see what will happen to taxi drivers, Uber drivers and things like that. Right. Over time. And so I think much more broadly, it's very interesting to see how these things will play out. But more specifically to us, I don't think where. I don't think it's necessarily as scary as it sounds on the surface."

He adds an important historical context: "I think if you look back through history, jobs evolve, change, disappear, created constantly. It's a constant shift of labor. And I think the most important thing is just being aware of what's going on, being open to new possibilities, new opportunities, regardless of AI or not."

This is a great vision, and I have no doubt Bryars and the team believe it. What worries me, is that it's the typical situation where companies would need to have guidance and structure to work on this problem/experimentation. Shouldn't HR be part of this process?

I see a ton of benefits for small companies (one or few people teams, like seed startups trying to find their product market fit at low costs). When moving to mid and up market though, there may be more downsides and second-order thinking that we'd need to assess. It's that delicate balance of experimentation / analysis that few get right.

Let's try and look at some of these elements.

Key Features and Benefits of AI Digital Workers

1. 24/7 Operation and Scalability: Alice and Jordan can work around the clock, significantly increasing the volume of prospects that can be reached.

2. Personalization and Tone Matching: The AI workers are designed to align with brand and individual communication styles, making their outreach feel authentic and personalized.

3. Integration Between AI Workers: The seamless handoff between Alice and Jordan allows for multi-channel communication strategies.

4. Efficiency: Bryars notes that managing Alice typically takes only "10 minutes a day, 15 minutes a day" for most users, allowing sales professionals to focus on high-value activities.

5. Continuous Improvement: The 11x.ai team is constantly refining and improving their AI workers based on user feedback and performance data.

Challenges and Considerations

1. Ethical Considerations: The question of whether to disclose the AI nature of these digital workers is left to the customer. Bryars mentions, "Alice is not known outwardly. Right. So like as an example, when 11x emails prospects, we of course use Alice because it's part of the brand and the product. But if you were signing up to Alice, emails would come from yourself."

2. Potential Saturation: As more companies adopt AI-powered outreach tools, there's a risk of oversaturating communication channels. However, Bryars argues that the quality and timing of outreach enabled by their AI can help counteract this trend: "I think outbound, regardless of AI, is always becoming more and more saturated. It's more and more accessible, even taking AI of the equation, there are platforms that allow you to source as many people as you want, send as many emails as you want, and so outbound effectiveness has changed over time. The most important thing is that with a platform like Alice or a digital worker, you're able to perform at a level that really helps change that trend in the sense that you can reach someone at precisely the right moment."

3. Evolving Sales Landscape: The nature of sales generation is constantly changing, requiring 11x.ai to stay ahead of trends and continuously adapt their offerings. As Bryars notes, "I do believe personally that how sales are generated will shift and change over time. And that's really a core focus of us as a business, is staying ahead of that curve."

4. Integration with Existing Systems: Ensuring seamless integration with a company's existing CRM, sales tools, and workflows can be complex and may require customization.

5. Data Privacy and Security: Handling sensitive customer and prospect data requires robust security measures and compliance with data protection regulations.

6. Training and Adoption: While the AI workers are designed to be user-friendly, there may still be a learning curve for sales teams to effectively manage and leverage these tools.

7. Performance Monitoring and Quality Control: Continuous monitoring is necessary to ensure the AI workers are performing as expected and not making errors that could damage client relationships.

8. Scalability of Personalization: As the volume of interactions increases, maintaining a high level of personalization and avoiding generic-sounding communications becomes more challenging.

Competitive Landscape

While 11x.ai is at the forefront of developing AI digital workers for sales and GTM operations, they are not alone. Several companies are developing similar or adjacent technologies:

1. Artisan AI: This company specializes in creating digital workers for various business functions within the technology sector, potentially competing with 11x.ai in some areas.

2. Attention: This is not an autonomous worker ‘play’ but more of a full-on automation of the call experience. Things like sending summaries, updating leads - all done by connecting the software to the sales call.

3. SDRx.ai: They have an interesting take focused more on research, like for example OrgInsight - which “Gains insights into a company's organizational structure by getting a detailed employee count by department in a single click”, or DISC - a buyer insight tool

Asked about their MOAT, Bryars says it's about execution speed. "We grew really fast, very rapidly by using our own product. We became our own best case study." This dogfooding approach has allowed 11x.ai to reach millions in Annual Recurring Revenue (ARR) primarily by leveraging their own AI workers. We are 10-15 times the size of the nearest direct competitor purely because we're moving at an insane pace."

I imagine this kind of company would also be worried about big tech adopting this approach inside their existing platforms (think of Salesforce, with their Einstein GPT - they already own a big chunk of the enterprise market). These enterprise software giants have several advantages that could allow them to quickly develop and integrate AI digital worker functionalities similar to what 11x.ai offers:

1. Vast amounts of data: Companies like Salesforce have access to enormous datasets from their existing customer base, which could be leveraged to train highly effective AI models.

2. Existing customer relationships: They already have established relationships with a large number of enterprises, making it easier to roll out new AI features to a broad user base.

3. Integrated ecosystems: The ability to seamlessly integrate AI digital workers into their existing suite of tools could provide a compelling value proposition for customers already invested in their ecosystem.

4. Substantial resources: These companies have significant financial and technical resources to invest in AI research and development.

Bryars acknowledges this potential threat, stating, "Competition for us is twofold. One, it's the large incumbents Salesforce. Really our vision here is becoming the system of record for GTM team Salesforce is much more of a competitor in that sense than an AI SDR."

AI Digital Workers - Future Outlook

What's 11x.ai's vision for the future of their AI digital workers?

1. Enhanced Integration: Bryars emphasizes the potential for even tighter integration between AI workers: "I think we've got a huge opportunity more in the inter-agent integration. Alice and Jordan. Jordan and whoever else. Those workers work in harmony, where it's not just finding a lead for you, it's an end to end GTM team finding a lead through to closing."

2. Expanding Capabilities: While currently focused on the early stages of the sales process, 11x.ai is exploring the potential for AI involvement in closing deals. As Bryars notes, "Right now I think that still needs a human in the loop. I don't think that will be the case forever."

3. Continuous Improvement: The team is committed to ongoing refinement of their existing products. "I don't think any of these things will ever be done. We will never reach a point where we're like, this is good enough".

4. Adapting to Market Changes: As the sales landscape evolves, 11x.ai is positioning itself to stay ahead of trends and adapt its offerings accordingly.

Final Thoughts

11x.ai represents a very interesting application of AI to sales and GTM operations. By creating AI digital workers capable of handling complex, multi-channel communications, they're not just automating tasks – they're trying to reimagine how sales teams operate.

In our last article, we outlined that there are many more ways to get work done, including digital workers future of work, as envisioned by 11x.ai, is one where AI and humans work side by side. But how will we get there, is the true question.

While specific data on AI digital workers like Alice and Jordan is limited, related trends in AI adoption provide insight into the potential market for these technologies. For instance, Master of Code Global reports that 23% of client service organizations now rely on AI digital assistants, indicating a growing acceptance of AI in customer-facing roles.

The impact of AI on worker productivity is also becoming clearer, but we're still in the infancy of proper academic (not consulting-style) research on the topic.

Ciao,

Matteo

Help me Improve Work3

Reinventing Work for the Digital Golden Age

Andy Spence — Wed, 09 Oct 2024 08:00:00 +0000

Are Transformational Revolutions Needed in the World of Work?

“…if AI's development unfolds in an unregulated financial market, its potential may not be fully realized. Transformational revolutions require mutually reinforcing innovations across technological, institutional, and economic domains.” Carlota Perez

Is AI Truly Revolutionary?

I asked this question in my article heavily leaning on Carlota Perez’s analysis of five tech revolutions of the last 250 years.

A reasonable response to this question, is, So What?

Does it really matter if AI is revolutionary technology or a just an evolutionary extension of the Digital Age?

For Work Futurologists, what matters is whether we can build transformational revolutions, and a new Golden Age for Work.

Perez’s analysis outlines that each Tech Revolution has an initial phase where a new technology is introduced. We then have excitement, investment, hype, and some wild extrapolation. The vested interests of Capital and Labour shift positions uncomfortably. Riches are made, there is new hope, then there are crashes, maybe riots. An example is the Industrial Revolution - triggered by new tech which enabled factories, attracted Luddite riots and eventually settled into the rise of the middle-class and the standard working week. The theory is after some excitement and chaos, a more positive Golden Age of assimilation takes place with broader societal benefits.

“Without a clear vision, supportive policies, and collective effort, the potential benefits of the digital golden age may remain elusive.” Carlota Perez

However, a Golden Age for Work in the era of Digital Tech is not inevitable.

We need a positive vision of what a better world of work might look like for the many - we need some mutually reinforcing innovations,

Here are some ideas to consider as we navigate the digital age…

The Future of Work: Trends To Watch

There is no universal Future of Work, like there is no universal Weather.

The futures of individuals and regions will depend on thousands of swirling factors at any given time. The variation is huge depending whether you are a Graphic Designer from Gothenburg, or you own Capital in Chicago.

Here are some trends impacting the future of work:

Shrinking Organisations: tasks become more automated, flexible contracts replace traditional jobs. As organisations can source work more broadly, they will need less employees.
Example from Banking - digital-only bank Revolut serves 25 million customers with 5,000 employees, compared to U.S Bank which serves 18 million customers with 77,000 employees.
There Will Be Many More Ways to Get Work Done: from independent groups of freelancers, to agencies, Guilds, work-matching platforms, or just good old-fashioned automation.
There Will Be Fewer Formal Jobs: The nature of work will shift, with less emphasis on traditional, formal jobs.
Workers Will Demand Better Work Conditions: 75% of Generation X have no plans of going full-time for their whole lives, according to a recent poll. Workers will demand better equity, more flexibility, and greater autonomy, leading to fewer bosses and offices in competitive industries.
Work Will be Managed With Diverse Work Contracts: not necessarily jobs, but projects, contracts, for different durations and remuneration. In HR terms, more attrition - or Churn, baby, churn.

Feel free to give your views, or share yours in the comments, or hit reply if you want to keep your comment between us.

While these trends paint a picture of a changing landscape, what can we do to ensure this leads to a Golden Age of Work?

Building a Golden Age of Work in the Digital Age

So what might the features of a Golden Age of Work look like?

As developing nations bring their economic metrics up towards G7 levels, it is apparent that what worked in the 20^th Century will not necessarily work for the rest of the 21^st Century.

The JOB worked with industrialisation, we built our societies around it, but I am not sure this will be the right model in the future. (You can read/watch more about my arguments in an essay and video)

We can’t predict the impact of technology including AI on work. I don’t think the nirvana will be Full Employment. Taking a Global View today, 6 out 10 don’t have a formal job. Although there is historically low unemployment, an increasing proportion of those in work are still poor.

There will be plenty of work to do, but it will be organised very differently.

“Work equity is already here – it’s just not very evenly distributed.”

HT William Gibson.

So we have a world with plenty of work, but less formal jobs, constant development in terms of what skills are needed, much more movement.

The successful will need to be nimble and entrepreneurial and demand equity, the less successful will need safety nets. Here are five pathways towards a golden age of work.

Five Pathways Towards a Digital Golden Age of Work

1. Decentralised work infrastructure for effective work-matching: Currently our career data is controlled by a few centralised digital landlords who charge fees for us to use our own data e.g. LinkedIn. Decentralised verifiable career wallets will allow individuals to manage their data and choose which work-matching platforms to use. Being able to find people who are good fit for your business, or for workers to find suitable work vacancies is a big improvement. It is hardly the Golden Age but a fix for the terrible infrastructure or plumbing we have now in the World of Work.

2. New Teams and Collaboration Models will Flourish: As jobs are deconstructed and unbundled into tasks, they will be rebuilt into new teams of people with mutually beneficial skills. Some new collaborative models and some old models renewed e.g. Digital Guilds and platform cooperatives. These teams will gradually pick-off chunks of work that are currently mainly done in-house by organisations. For workers, some more flexibility, possibly more equity and autonomy.

3. Lifelong Career Investment for a Dynamic Workforce: We cannot predict what skills will be needed by in the labour market in a few years time. But one things for sure, studying for 20 years then working for the next 50 without further training will not be feasible. Successful societies will re-train and re-skill their adult populations.

Some examples:

Singapore are investing in SkillsFuture Credit for Mid-Careers support for 40-60 year olds.
In France there is the Compte Personnel de Formation (CPF), with a lifetime cap of €5000-8000
Canadians have the Lifelong Learning Plans (LLP) which allows withdrawal from retirement savings – up to $10,000 per year for education
The European Union has a goal for 60% adult population by 2030

There are some interesting paths this might go with AI-powered learning pathways, and verifiable learning records. How this might be funded and distributed in the future will be a debate for policy-makers.

4. New Mechanisms for Financial Security: If full employment is unrealistic for most countries, we need new mechanisms that replace what the JOB provided, e.g. financial security, meaning, and purpose. During COVID-19, mass payments were made to families, hinting at the potential for a universal basic income or similar systems. With less income from jobs, last week we shared the latest research on Universal Basic Income. (You can still vote on whether you think it is a good idea). We might also need Universal Basic Services too, with states providing more diverse public services.

5. More Support for Entrepreneurialism: Lifelong Learning Budgets, Universal Basic Income and Services all cost public money, where will this come from? Well these smaller organisations making more profits with less people will be asked to pay more taxes, that’s a given. With less formal jobs and less interest in having a boss and working in an office 5 days a week. More people will develop their side hustle, start businesses and find other ways to pay the bills. This has become easier with Digital Platforms. We have groups of Solopreneurs using Shopify or Etsy, or Playbourers (play and labour) making a living entertaining or providing insights.

The audience here is diverse, please share your thoughts on policy ideas that are worth considering in the comments.

Towards a Golden Age of Work

To talk in a meaningful way about the Future of Work we need to be specific and ask Who for? We cannot discuss the Future of Work without talking about what kind of society we want to live in.

Whether we can build a Golden Age for Work will depend on the context, but will also rely on mutually reinforcing innovations across technological, institutional, and economic domains.

A future of work with more flexibility and equity for workers, more variety of contracts and assignments, and some freedom for starting businesses will need support from the public sector.

It might not seem like a dreamworld, but it can be a realistic vision to build a more inclusive future of work.

A golden digital renaissance can usher in a new era of prosperity, innovation, and empowerment for individuals and society as a whole.

It’s up to us.

I would love to know your thoughts on this!

Not quite in the Golden Age,

Andy

Help me Improve Work3

Future of Work Digest #1

Andy Spence — Wed, 02 Oct 2024 08:00:00 +0000

Welcome to this edition!

This week, we ask:

As Amazon employees return to the office, does this mark the end for WfH? With the latest study on the benefits of Hybrid working.
Is Universal Basic Income a good idea?
How can we make work more sustainable?

Plus we are delighted to share an Exclusive offer to Subscribers – free lifetime access to Global Leaders Summit digital library of presentations!

Let’s dive in 👇🏻

Eyes on Amazon as staff work from home days are now 'Out Of Stock'

Photo Source - Getty Images

Amazon is ordering staff back to the office five days a week as it ends its hybrid work policy. With over 1.5 million employees, strengthening culture, teams and refining organisational performance must be a challenge for its leadership.

The change at Amazon is positioned in the context of reducing the number of managers to decrease bureaucracy. They want to operate like the world’s largest startup when they are the world’s 2^nd largest employer.

Hey team, you can read the full memo from Amazon CEO to staff here.

While it looks like the days of Hybrid working are past their Prime - a new study reveals some of the benefits.

Benefits of Hybrid Working – Results of a New Study

The top 3 benefits of working from home according to research from Professor Nick Bloom are no more commuting, savings costs, and more flexibility. (you can read his latest report ‘The Future of WFH’ - 32 page PDF presentation )

The results of a new study have been published in Nature and its the first randomised controlled trial of the benefits of hybrid working.

More than 1,600 graduate employees of Trip.com were randomly assigned to one of two groups for a six-month period, with members of one group working five days a week in an office, and those of the other working three days a week in the office and two at home.

The authors found that the hybrid workers generally had:

increased job satisfaction
reduced resignation rates
were as productive as those who worked entirely in-person, as measured by performance reviews.
reduced attrition rates were not seen for managers, who made up one-quarter of the trial sample

Randomised controlled trials are a gold standard for determining the answers to these types of questions. But for work designers, be careful with extrapolating the findings too widely as not every company resembles Trip.com – a multinational headquartered in Singapore with 45,000 employees.

CHRO TIP - an important mantra in these swirling times is DYOR – Do Your Own Research. Evaluate what works best for your workforce and context by using workforce data and running experiments. Adopting an Evidence-Based Management approach can be helpful for example, randomised controlled trials are a better source of evidence than anecdotal evidence from your rivals. When you have to propose your recommendations to the Board, this approach can help avoid the worse mistakes and help you sleep better at night.

For a deeper look, you can read the full Nature article here.

Also one of the real reasons why employers are calling workers back to the office might surprise you - read our Peak Office article to find out more.

Amazon pays salaries to 1.5 million people providing some financial security, but what about those who do not have reliable income?

Is Universal Basic Income A Good Idea?

We frequently discuss the potential impact of AI on Jobs. Some jobs will be lost for sure (see for example, Tech Layoffs in 2024) but one area of job growth is an army of management consultants implementing ‘Gen AI’ for organisations. Accenture reported $3 billion fees for Gen AI last year and plans to increase its data and AI team to 80,000 consultant by the end of 2026.

The business model for Big Consultancies is consistent and simple – generate demand for new technology as it evolves. We have seen this for Cloud computing, mobile, Big Data and now Gen AI. With all new technology we need to critically assess the marketing claims and hype.

Big Tech and associated industries on the bullish side of the argument will tell us that AI will rip out human work over the coming years. In this scenario, income will dry up from traditional employment which generates some tricky question:

How will people feed their families ?

The conversation around Universal Basic Income (UBI) gains momentum. The idea is that the state provides every individual with the minimum income required to live. It is not a new idea, Thomas Paine, proposed something similar in the 18^th Century.

Since then there have always been voices from diverse ends of the political spectrum:

From the left – ‘everybody should have enough to live on’
From the right – ‘we should reduce government welfare bureaucracy’

What happens when AI inevitable takes our jobs has prompted Tech companies to fund a new study. This might be motivated by survivors’ guilt, or to answer another question, How can we avoid Universal Basic Unrest?

Sam Altman, CEO of OpenAI, which makes ChatGPT, has launched UBI experiments through his initiative, GiveDirectly,

5,000 participants across two U.S. states receive $1,000 per month for three years.

The researchers highlighted some findings, such as participants:

used the funds to buy essentials like food, rent and transportation, not vices.
cut down on taking un-prescribed painkillers and drinking too much.
had 26% more hospital visits than the control group.

There have been plenty of other UBI studies and they raise lots of questions.

For example Finland’s 2017–2018 UBI trial involved 2,000 unemployed citizens receiving €560 monthly. Results showed improved mental well-being, but no significant impact on employment rates.

Kenya’s ongoing UBI trial, run by GiveDirectly, involves over 20,000 people. It’s one of the largest studies, yet its outcomes in a developing economy might not translate to wealthier nations.

My main issue with these trials, is the experimental hypothesis,

How can we assess whether UBI is a good idea, unless it is actually Universal?

Otherwise these are localised studies, with small samples where the participants live in an economy where others do not have the same safety blanket.

How Can We Make Work More Sustainable?

Some Turkish Delights in Istanbul

Last week I was honoured to deliver a keynote speech in Istanbul at the Global Leaders Summit. The theme was AI and Sustainability and my talk was “Building a More Equitable and Sustainable Future of Work – Myths and Megatrends”.

John Elkington, known for the Triple Bottom Line concept (Profit, People, Planet), framed the event by outlining some of the critical sustainability challenges of our times. From Natural resources shortages to misinformation, involuntary migration and societal polarisation amongst others.

I emphasised that while humans have caused many of the sustainability problems we face, we must also be the ones to solve them. This will come from diverse teams working creatively. By improving how we work, we significantly increase our chances of addressing these complex challenges.

You can read a bit more about my talk here.

And for those interested in watching some interesting speeches from the event, we have an exclusive offer…

Exclusive offer for Subscribers!

I’m pleased to share with you a complimentary invitation link to register for the Global Leaders Summit 2024 online sessions. This exclusive also provides lifetime access to the digital library of presentations.

Gain Access Here

We hope that you enjoy this weeks edition of the newsletter, please let us know what you think by commenting or hitting reply to us what you really think! And sharing this article with your network who are interested in building a better future of work helps us extend our offerings over the coming months.

Full of Turkish Delight,

Andy

Help me Improve Work3

AI’s Next Leap: Human-Like Reasoning and Its Impact on Employment

Matteo Cellini — Wed, 25 Sep 2024 08:00:00 +0000

Source: CodeSignal

Hello and welcome to this week’s edition - Let’s dive in!

Will AI Make The Leap Towards Human-Like Reasoning ?

Last week’s headline was hard to miss: OpenAI has launched its latest model, GPT o1, which introduces 'chain-of-thought' or 'human-like' reasoning capabilities. Unlike its predecessors, GPT o1 takes more time to generate responses as it meticulously works through multiple reasoning steps before arriving at an answer. Perhaps the OpenAI team took inspiration from my previous post on how AI can enhance thinking through the Socratic method? 😃

To visualize this paradigm shift, Jim Fan shared an insightful graphic where the strawberry symbolizes GPT o1, referencing its codename 'strawberry':

While today's focus isn't to go deeply into GPT o1, several questions have been on my mind regarding the widespread adoption of this approach:

Creativity and Innovation: How will reasoning AI reshape the boundaries of creativity and innovation in generative AI models?
User Experience: What changes will we see in the user experience when interacting with AI models that exhibit deep reasoning capabilities?
Long-Term Problem Solving: What new possibilities emerge when reasoning AI is given time to process complex problems over extended periods, such as days or weeks?
Resource Allocation: What considerations will companies need to make when balancing compute resources between training large reasoning models and running real-time inference?
Ethical Implications: How do reasoning models align with the broader, long-term vision of AI, and what new ethical questions arise from their capabilities?

Additionally, referencing another comparison chart where GPT o1 outperforms expert humans on PhD-level science questions, I considered some key obstacles that this technology might face when operating autonomously:

Dependability Over Time: While GPT o1 may excel initially, maintaining reliability over extended use could be challenging, potentially leading to errors and inefficiencies in long-term workflows. This might require more human oversight than anticipated. How do we plan for this?
Handling Complex and Evolving Tasks: Although GPT o1 is proficient in specific areas, it may struggle with more intricate and dynamic problems, limiting its effectiveness in environments that require adaptability. As we move forwards we will need more complex tasks to be handled. It won’t be just about computing power (?)
Balancing Human Expertise and AI Assistance: An over-reliance on AI could result in a decline in human skills and expertise. This, in turn, will leave more room for errors. Who’s going to check this, as it increasingly becomes a black box? It’s more of a problem if the ‘inference’ stage can’t be analyzed.
Ongoing Maintenance and Fine-Tuning: To remain effective, models like GPT o1 need continuous retraining and updates. This requirement can escalate operational costs and complexity, will this be always positive in the cost/opportunity balance?

So how close is AI to the capability of human engineers?

A new study pits leading AI models against human engineers in real-world coding challenges. These challenges mirror the technical assessments used by major tech companies for hiring.

Key findings reveal:

Advanced AI models surpass the coding skills of average human engineers.
While top human coders still outperform the best AI models, the gap is narrowing.
AI models show dramatic improvement when given multiple attempts at a problem.

You can read the Open AI Report, from here.

And the AI Coding Benchmark with Human Comparison Report, here.

So are companies actually saving money with efficiencies driven by AI?

AmazonQ saves 4,500 years of Coding

Amazon CEO Andy Jassy talks about the impact of their AI coding Assistant, ‘Q’.

In his Linkedin post, he mentions that it helped the company upgrade its Java versions and saves a total of 4,500 years of coding. Also, in a leaked chat, Matt Garman, CEO of AWS, told employees that AI's growing role could fundamentally change what it means to be a software engineer. Suggesting that as AI takes over more of the coding work, developers will need to shift their focus from writing code to understanding customer needs and designing the end product.

"It just means that each of us has to get more in tune with what our customers need and what the actual end thing is that we're going to try to go build," Garman said.

AmazonQ Performing a Code Deployment

Techno-Magicians or Wild Extrapolation?

Of course, GPT o1 represents a significant advancement in AI tech. However, our inherent tendency to think linearly rather than exponentially can skew how we apply it. We're often so captivated by the allure of techno-magic that we implement it without fully understanding its benefits, similar to the initial excitement around GPT-3. As Andy noted in our last article, “Is AI Truly Revolutionary?” we are probably in the ‘Wild Extrapolation’ phase right now.

It's not to say there haven't been any, but our attraction to shiny new tools can make us overestimate our objectivity and rationality.

As Nassim Nicholas Taleb aptly puts it,

“what is rational is that which allows for survival… Anything that hinders one’s survival at an individual, collective, tribal, or general level is, to me, irrational.”

This perspective highlights that what we consider rational is often driven by survival instincts and economic imperatives, leading to the continuous creation of new products—even those we may not need. In our quest to stay ahead, we sometimes neglect to assess whether these innovations genuinely benefit us or if we're merely chasing the next big thing. Therefore, while new technologies like GPT o1 hold great promise, it's essential to critically evaluate their long-term benefits and potential pitfalls instead of getting swept up in the hype.

That’s all for this week - let me know if you have other interesting news or submissions you’d like to discuss in the comments!

Ciao,
Matteo

Help me Improve Work3

Planning for Non-Linear Career Trajectories

Matteo Cellini — Wed, 18 Sep 2024 08:00:00 +0000

Welcome, curious minds!

This week we have a short double interview through which we explore the future of work through the eyes of two thought leaders: Paul Millerd, author of the "Pathless Path" philosophy, and Al Dea, a former management consultant turned entrepreneur specialising in talent and leadership development who hosts the popular podcast The Edge of Work.

Let's dive into their perspectives on various aspects of the evolving work landscape with a short commentary and food for thought/questions.

Planning for Non-Linear Career Trajectories

Source: Linkedin CEO Ryan Roslansky

We’ve talked about how non-linear careers before (here ‘Quarter-Life Breakthroughs, AI Companions and Lateral Careers and here ‘Develop 100 different lives for career success) which in my personal opinion should be a subject taught in schools. We need to be ‘antifragile’ (yet another concept by Nassim Taleb - it describes systems that not only benefit from disorder, uncertainty, or volatility but actually thrive on it)

Paul Millerd: "On a pathless path, I think you realize that life is always a learning process whether you are employed or not. From the outside it looks like this path is risky but that's only because the costs are obvious: fear, lack of support, financial uncertainty. But once you're on a path like this you realize that being aware of those things all the time can provide a lot of wisdom about life, or at minimum, teach you that there's not much to be afraid of."

Al Dea: "My career journey is a testament to the importance of adaptability and innovation in the face of change. I successfully transitioned from a traditional corporate role as a management consultant at Salesforce to a thriving independent entrepreneur, specialising in talent and leadership development. This transition, which I made during the early days of the COVID-19 pandemic, was driven by a desire for greater autonomy and the opportunity to focus on my passions."

Going back to the ‘Antifragile’ concept, both Millerd and Dea emphasize the importance of embracing uncertainty and change in one's career. Millerd's "Pathless Path" philosophy suggests that the journey itself is the destination, and Dea's experience highlights the potential for significant career pivots even in challenging times. These are times of growth, when you’re going outside of your comfort zone.

Food for thought: How might embracing more uncertainty in careers lead to unexpected opportunities? How would one go about evaluating this when hiring?

On Education and Lifelong Learning

Big topic, also recently covered in The Death of Degrees and the rise of Company Universities. As seen from the chart above, skills can be very unstable and new ones come up in very short timespans. My key question here, which I’m obviously asking myself pretty often since I’ve become a father is: ‘how can you prepare your kids for the increasingly complex, uncertain and competitive world of work out there?’

Paul Millerd: "With our daughter, we hope to just help her get to know herself better and root for her to follow her own interests and curiosity. I don't really have any 'goals' for her in terms of outcomes like fancy colleges, private schools, or impressive jobs, but there are things we value in our family like kindness, generosity, and curiosity, and I hope we can help her cultivate those things too."

Al Dea: "In a world where technology is rapidly advancing, it's more important than ever to invest in employees' development. By creating a culture that fosters creativity and innovation, we can unlock the full potential of our workforce. I believe that organizations must foster a culture that values creativity, innovation, and critical thinking."

Food for thought: This is a tough one to balance. How might educational institutions balance the development of practical skills with the cultivation of personal qualities like kindness and curiosity? In what ways can organizations create a culture of continuous learning that goes beyond job-specific skills?

On AI and Human Skills

source: disneyartonmain

Paul Millerd: "I'm using Claude to help me in writing now, mostly to help generate different ideas of writing sentences or synonyms or tone differences and things like that. It's like a writing coach. I also use ChatGPT for cooking which is excellent at answering my beginning questions."

Al Dea: "Even as AI becomes more sophisticated, there will always be a need for human empathy and understanding in customer service. It's not necessarily about choosing either or, but really trying to figure out what exactly are the strengths and capabilities that are at play and who is uniquely positioned to do them. In my case of use, how I've been using AI, I've tried to actually use it. A lot of people say, let AI take the first pass. I've decided to use it. You know what? I actually want to take the first pass in this case because I feel pretty competent in what I'm doing, but I do want to augment it with something else."

Both Millerd and Dea see AI as a tool to augment human capabilities rather than replace them. They emphasize the importance of understanding how to best leverage AI in various contexts, from creative work to customer service.

Food for thought: How might the integration of AI in your daily work change your role or the skills you need to develop? In what areas do you think human skills will remain irreplaceable, and how can we ensure these skills are valued and developed in an AI-driven world.

How do we tackle Demographic Shifts?

Paul Millerd: While we didn’t talk about this directly with Paul, his philosophy indirectly speaks to adapting to changing workplace dynamics.

Al Dea: "The demographic shifts we are experiencing are unprecedented. As the workforce ages and becomes increasingly scarce, businesses must adapt their strategies to attract and retain talent. In many developed countries, the population is aging rapidly, leading to a shortage of skilled workers in certain industries, such as healthcare and technology. I suggest that businesses may need to invest in training and development programs to upskill their existing workforce or explore alternative talent acquisition strategies, such as hiring from overseas."

I’m particularly interested in this aspect, given how ‘underrated’ it seems to be in the news and books I read on the topic. Ageism is already a big problem both in job search and in the workplace and it’s incredibly tough to combat. How do you break that discrimination when there are no real tools to do so, and in an increasingly competitive market?

Food for thought: How might companies need to adapt their cultures and practices to accommodate a multi-generational workforce? What opportunities might arise from tapping into previously underutilized talent pools?

On Solopreneurship and Independent Work

Both Paul and Al are now solopreneurs. This is a rising category of workers, and even more so with AI.

Paul Millerd: "I miss the team-based work with smart colleagues working at a fast pace. It probably happened on 3-4 projects over 10 years. I also miss being able to be part of the training teams at some of my jobs and being able to fly around the globe in business class doing those. I don't miss 80% of it though."

Al Dea: "I can't tell you how helpful AI tools have been for that, particularly as someone who is a one person shop. I can literally type in, summarize show notes and they will within two to 3 seconds give you a summary of the show notes. I very much view my creativity as my unique engine that enables me to do the work that I do. But I know that I alone am not the most creative person in the world. And so in many ways I'm using it to check myself as well as to potentially augment or to get something in my head to bounce something off of it. So maybe I can think of something even better."

Food for thought: How might the rise of solopreneurship and independent work change the nature of collaboration and innovation in the future? What support systems or technologies might emerge to address the challenges faced by independent workers?

On Key Skills for the Future

The change that will come with AI will require honing on different (and in many cases new) skills. We will need to master how to prompt and use the machines, but then soft skills will become critical.

Paul Millerd: "People think far more communication is taking place when they present information than they think. Unless you are clearly saying simple and powerful things, an audience of 10 people will think 10 different things about what you think."

Al Dea: "In my experience, the most crucial skills for the future workplace include adaptability, creativity, and effective communication. As AI takes over more routine tasks, our uniquely human abilities to innovate, think critically, and connect empathetically with others become even more valuable. I've found that fostering these skills within organizations can lead to more engaged employees and better business outcomes."

Comment: Both experts emphasize the importance of effective communication, with Millerd focusing on clarity and simplicity, while Dea broadens the scope to include adaptability and creativity. They both recognize the increasing value of distinctly human skills in an AI-driven world.

Food for thought: How might education and training programs need to evolve to develop these key skills for the future workplace? In what ways can organizations measure and reward these soft skills alongside more traditional performance metrics?

Final Thoughts and Learnings

Paul Millerd: "People should aim to take a sabbatical sometime in their career, if only to double-check that they are really living the life they want. Too many older people regret how they spent their prime years of their life. Taking three months off is very doable - humans achieve far more challenging goals like buying dream homes, cars, and funding college educations, all the time. Just find 3 months to take off."

Al Dea: "The pandemic has led many to take control of their work narratives, seeking to define their own stories rather than letting others dictate them. I've observed that understanding one's work story is essential for finding meaning and fulfillment in work. As we move forward, I believe it's crucial for both individuals and organizations to embrace change, prioritize continuous learning, and focus on creating value through our uniquely human capabilities."

Comment: Both Millerd and Dea advocate for intentional reflection and self-directed career development. Millerd suggests a sabbatical as a way to reassess one's life choices, while Dea emphasizes the importance of crafting one's own work narrative.

Food for thought: How might organizations benefit from encouraging employees to take sabbaticals or engage in deep reflection about their work? What strategies can individuals use to continuously reassess and align their work with their personal values and goals?

That’s it for today - hope you found these points of views and reflections useful, but would love to know your thoughts, drop a comment or reply!

Ciao,

Matteo

Help me Improve Work3

Is AI Truly Revolutionary? A 250-Year Perspective on Tech Disruption

Andy Spence — Wed, 11 Sep 2024 08:00:00 +0000

With every major technology change we get swept up in the hype.

As futurist Roy Amara once noted,

“We tend to overestimate the effect of the technology in the short run and underestimate for the long run.”

Every new technology follows a similar path:

Weak Resistance
Genuine Excitement
Wild Extrapolation
Quiet Assimilation

AI is firmly in the ‘Wild Extrapolation’ phase now.

Every day we hear stories of AI tools like ChatGPT doing tricks for us:

“Create an image for this article in the retro-futurism style using blue and green tones.”
“Please conjugate the 20 most frequently used verbs in European Portuguese.”
“How can I travel from Lisbon to Rome then to Istanbul, without buying a larger belt for my trousers?”

But beyond my own personal usage of AI, I also have daily conversations on how AI will impact employment, from:

The graphic designer I don’t call anymore - until ChatGPT can’t add text.
The language teacher amused at my Portuguese language proficiency - which is only used in Bahia state in Brazil.
The travel agent who is reinventing personalised travel for the feckless.

This article briefly explores whether AI constitutes a revolutionary step-change or just a continuation of the digital age that began in the 1970s.

Thinking about the future - sometimes it can help to look at the past.

Make sure you Subscribe to get future articles directly to your inbox!

Five Tech Waves That Changed the World

According to Carlota Perez's theory, there have been five significant technological revolutions since 1771 that have driven economic and societal change.

the Industrial Revolution (1771)
the Age of Steam and Railways (1829)
the Age of Steel, Electricity, and Heavy Engineering (1875)
the Age of Oil, Automobiles, and Mass Production (1908)
the Information-Communications-Technology (ICT) Age (1971).

According to her theory, each surge follows a similar pattern:

Installation: new tech emerges, causing excitement and often with financial bubbles and eventual crashes e.g. The railway mania in the 1840s
Deployment: the tech matures leading to increased productivity and societal benefits, often referred to as the ‘Golden Age’ e.g. the rise of cars led to the growth of related industries like oil and steel, which, in turn, elevated living standards.

Here are some more resources to fully explore Carlota Perez’s theory

So where does AI fit into this grand scheme?

Is AI a technology revolution or more of a digital evolution?

In Perez’s theory, AI is best understood as a key development in the still-evolving ICT revolution that started in the 1970s. Technological revolutions involve major transformations within government and society, not just advancements in technology.

“Everyone is talking about artificial intelligence as though it represents the next technological revolution. In fact, it is better understood as a key development within the still-evolving information-communications-technology (ICT) revolution, which started in the 1970s with the microprocessor.” Carlota Perez

I am with Carlota Perez, AI is an extension of the longer-term Digital Age:

AI builds on decades of progress in algorithms, natural language processing, and data analysis.
AI leverages existing infrastructure which was already here, unlike previous technology revolutions which required massive infrastructure e.g. railways or electrical grids.
AI augments and enhances human capabilities, not fundamentally changing how we live and work (yet).

What Does This Mean for the Future of Work?

There are genuine fears of unemployment from AI and not just from Graphic Designers and Teachers. Although we cannot predict the future accurately we can envision that jobs will unbundle and work will be re-organised into different types of contracts.

Work Will Not Disappear - there’s a lot more to do!

Previous technological revolutions created fears about unemployment.

From The Luddite movement back in 1811 destroying the automated looms to Hollywood Writers last year worried about AI writing their Netflix scripts.

Another example is the introduction of spreadsheet software, Lotus 1-2-3 and Microsoft Excel in the 1980s.

With the introduction of this software where you could instantly recalculate revenues and profits with one click - there was widespread fear amongst the bookkeepers that they had crunched their last number.

As the graph shows, the new technology did reduce the demand for bookkeepers. Yet the demand increased for people who could build and run these spreadsheets using the new software.

Human Skills Will Still Be in Demand

The machines will change how we work, but it’s better to master them than be replaced by them. The automation trend will continue, but thankfully us humans still have something to contribute.

The Future of Jobs Report 2023, from the World Economic Forum, found the top skills needed by employers include creative thinking, motivation and self-awareness, and curiosity and lifelong learning. However sophisticated LLMs become - they will never evolve to develop the motivations that have kept us humans curious.

We are still in the early stages of AI adoption, so we will see the impact of over the next decade or so.

Stay tuned for our next articles which will consider in more depth,

Will AI Take Our Jobs? and How might we build the ‘Golden Age of Work’ where we see significant improvements to broader society?

We are not just witnessing history as passive recipients – we're shaping it.

Let's approach AI with informed optimism and a healthy dose of critical thinking.

What are your thoughts on AI and the future of work? Share in the comments below!

Looking back to look forward,

Andy

Help me Improve Work3

From Human to Robot Resources: Are We Entering the AI Summer of Love?

Wed, 04 Sep 2024 08:00:00 +0000

Welcome, humans!

Exciting News!

Today’s piece is a special edition, as I have a big announcement to share, in the spirit of building in public.

The future of work is something we build together. In this spirit of collaboration, we have agreed with my friend Andy Spence at Workforce Futurist, to join forces and create a single bigger + better publication.

Workforce Futurist is a publication on the Future of Work with more than 4,000 subscribers, so our new joint community will be reaching more than 22,000 total CHROs, CEOs, Journalists, Professors, Solopreneurs, Web3 types, Job seekers, leaders and loafers, and more all over the world 🌎

I am incredibly excited about this 🎉 for two reasons:

✏️ Andy is a fantastic author and long-time expert in the field of HR and innovation — some of you may already know his work, He has 25+ years of experience in the field, and having him onboard allows us to publish even better pieces by working together on research and writing.
📬 Joining efforts will result in more quality content for you — I had shared in the past my plans to roll-out a podcast, a community, and more but this simply wasn’t possible with my full-time job (and family 🍼)

What you can expect from now on:

Weekly schedule of: curated future of work topics and news, plus feature articles and interviews
Product and community launches in the upcoming future

What can you do for us:

Nothing; sit back and enjoy
Get in touch if you’re interested in partnering and sponsoring the newsletter
Share your inspiring stories and challenges so that we can address them in the upcoming issues

We are looking for collaborators, agitators and partners to build new products and services for the community. Please help us spread the word by sharing with friends and colleagues - your support is vital for us 🙏🏻

Subscribe if you haven’t already done so 🙏🏻

In the first of our weekly work trend updates, we ask:

Are we in the AI Summer of Love?
Can AI transform a large government department and,
Is HR about to become RR – Robot Resources?

Let’s dive in!

1 - The AI Summer of Love?

ChatGPT is useful and can be fun - for example, converting your WhatsApp group messages to the ‘Downton Abbey style’.

However, hundreds of millions have tried it once, but not returned.

In his article ‘The AI Summer’, Benedict Evans makes a couple of observations about LLMs (Large Language Models) like ChatGPT. He argues that LLMs look like ‘ready-made products’ – partly because of the reassuring natural language interface. However building products that sell is much harder work involving the right marketing, bundled offerings, user interface etc.

It doesn’t mean that LLMs are not being used, they are, but the adoption story will be mixed. Looking back, it turns out that ‘the future’ can take longer than we think. For example,

It took 20 years for 20% of US Retail to move online.
Only 5.4 million iPhones were sold in the first 12 months after launch.

Evans gives the example of consulting and tech firms making dubious claims to piggyback on Peak AI. He makes the argument that the adoption of LLMs might follow a different path to the previous hyped technology.

In the world of work, I think LLMs accelerate the existing and longer-term mega trends - for example, increasing the proportion of people working in non-traditional forms of employment. More by doing tasks quicker and more cheaply than creating the next killer product.

Watch this space for more exploration of Peak AI in the coming weeks to see whether AI will fizzle out like Hippy hair-styles, or be a game-changer.

Read the full article here.

Poll - Are We In the AI Summer of Love?

2 - Can AI Transform a Large Government Department?

The UK’s Department for Work and Pensions (DWP) is the largest public service department in the country and responsible for administering welfare and pensions to 20 million citizens.

DWP employs nearly 90,000 people and costs more than £240 billion ($308 billion) to run every year.

Like most business functions, someone has been tasked with looking at AI opportunities. According to the DWP Director of AI,

“AI will allow us to create a more human service that cares better for the vulnerable, provides colleagues with more rewarding jobs and takes the pressure off public finances so money can be spent where it matters most.”

A research paper indicated that the DWP workforce could free up as much as 40% of its time using AI tools. This is equivalent to a productivity gain of close to £1 billion a year. They could reduce backlogs for paying citizens and introduce chatbots to help people find the right job or training.

The challenge here will not be the delivery of AI technology – which is mostly an extension of data processing, statistics, natural language and algorithms. The challenge is delivering organisational change - how people work, the processes, the culture - which in my experience is much more complex than implementing new technology.

Curiously, DWP has issued guidelines formally prohibiting employees from using ChatGPT. On the one hand, it has ambitions to use technology to improve its organisation, on the other hand, it doesn’t empower its own workforce to innovate - which is where the most promising ideas usually come from, not from external consultancies.

This is a reminder that the success of new technology implementation is very much integrated into the organisational context.

There are loads of compelling AI ideas in this type of proposal with tempting examples from around the world. However, we need to be sceptical of productivity gains “from AI” and learn from previous technology innovation attempts in the context of these organisations.

You can read the research paper, here.

3 – From Human To Robot Resources 🤖- AI Agents as Co-Workers

The debate about integrating AI into the workforce reached a boiling point recently when HR software company Lattice proposed giving AI agents official employee status. The idea was to fully integrate these digital workers into the company’s organisational structure, complete with onboarding, performance metrics, and even managerial oversight.

On first glance this looks like yet another case of AI anthropomorphism – the tendency to view inanimate things with human characteristics.

We humans can see a shovel doing work and know it’s a tool, but if we put a cute face and name to an app with software and data behind it skews our thinking.

And our AI engineers do like to put a human face on our technology, usually a woman’s face.

With names like Eva, Vera, Jane, Olivia, Ally, Mya - so it only seems fair to give these helpers employment rights like us right?

Technology Conference, Las Vegas, 2018 Source: Andrew Spence

The organisational context comes from a profession named ‘Human Resources’ which linguistically anyway, deems people as resources to be utilised, like erm shovels.

Anyway, the bold move from Work Tech company Lattice was retracted after facing significant backlash from HR professionals and industry experts. Lattice made a mistake, but this does pick up on a challenge as HR evolves into its role of ‘work designers’ rather than workforce process managers.

Organisations need to design work and use the best resource available whether that is from existing staff, agencies, or automated solutions. We now have many more options beyond filling vacancies. From freelancers to agencies to robots, DAOs and automation opportunities are growing exponentially. I think we need a major shift in mindset and capabilities from managing employees to designing work in its broadest sense.

Our current Work Technology stack is poorly equipped to support this move as it is mainly 30 year software architecture built on top of 20^th century management processes.

Do we currently have the right work infrastructure to manage this? Absolutely not!

But we have a significant opportunity to shape this area and help make work better for more people...

Finally, the thought of Human Resources expanding to include Robots🤖is not a new one!

We’d love to hear your thoughts – let us know in the comments.

Watching the work horizon,

Andy

Work3 - The Future of Work

35 New AI Roles to watch out for

1. AI Specialized Roles

2. Human-AI Roles

3. Innovation and Augmentation: AI Enhanced Roles

Challenges and Final Consideration

Help me Improve Work3

35 New AI Roles to watch out for

1. AI Specialized Roles

2. Human-AI Roles

3. Innovation and Augmentation: AI Enhanced Roles

Challenges and Final Consideration

Help me Improve Work3

Five Reasons To Be Optimistic About Work in 2025

Global Hiring Outlook: Growth Signals for 2025

The Misery Index is Low

The Pyjamification of Work

More Ways to Earn

More Ways to Learn

Help me Improve Work3

The Best of 2024: 10 Articles That Shaped Our Thinking About the Future of Work

1 - Death of the CV

2 - Building a Better World Without Jobs

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

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

5 - Retiring Retirement

6 - Reinventing Work for the Digital Golden Age

7 - Will Digital Workers Revolutionise Work?

8 - The End of Typing

9 - 2034 – A Day in the Working Life

10 - Planning for Non-Linear Career Trajectories

5 Big Questions for Work in 2025

1- How Will AI Agents redefine work?

2- Is the Office dead or just evolving?

3- How Will the Big Tech backlash impact work?

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

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

5 - What constitutes good career advice in 2025?

Help me Improve Work3

Building a Better World Without Jobs

A Brief History of Work

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

Walls Come Tumbling Down

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

Building a New Social Contract and Infrastructure

What A New Infrastructure For Work Might Look Like

Building a Preferable Future

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

Help me Improve Work3

Future of Work in 2024 – Latest Scores

Capital 1 - 0 Labour

Join Our Webinar - The Big Questions for Work in 2025

Robots 1 - 0 Humans

Salarymen 0 - 1 Freelancers

Anywheres 1 - 1 Somewheres

Technology 1 - 0 Trust

Centralised 0 - 1 Decentralised

Office 1 - 0 Home

A Personal Note to Our 25,000 Subscribers

Help me Improve Work3

Beyond Prediction: Shaping Your Work Story in an Uncertain World

Seven Competing Narratives about the Future of Work

1 - Dataism - Trust the Algorithm

2 – Exterminism - The Robots Take Over

3 - Re/Upskilling - Learn or Be Left Behind

4 – Augmentation - Humans and Machines in Harmony

5 - The Singularity - When AI Surpasses Us

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

7 - Work Deintensification - The Age of Leisure

Poll: What Narrative Do You Believe In Most?

The Next Chapter of Research

The Future of Work is What We Make It

Future Reading

Help me Improve Work3

The End of Typing?

Death of the Keyboard

Advanced Voice Mode: What is it

Why Voice is the (obvious) last frontier of AI

Key Applications in the Workplace

Voice and Workplace Simulations