Welcome to the New Renaissance

Creativity & Knowledge in an Artificial World 

Introduction: Challenging Our Understanding of What We Know

How do we define intelligence and creativity? How do I know you are smart? Are you actually ever being original? These are some of the questions that teeter of the philosophical but need to be asked in order to understand how Generative AI is fundamentally changing what we conceptualise as knowledge, intelligence and creativity. For those like me with a classical education going through the rounds all the way from Primary to Further Education the gauge of our intellect has been forever tied to and measured by our ability to answer questions, ranging from simply recalling a historical date to writing essays of the metaphysical interpretation of a John Dunne poem (I hate that flea).

For a long time, the measure of how smart a person is, has been based largely on their recall and ability to debate a subject in an eloquent enough way to suggest they have committed some thought to the matter and that directly leads to attainment of pieces of paper that provide the suggestion of intellect. Now I’m not being completely derisory of the education system but there are questions that need to be asked? Of course, in this world there are a multitude of people who are Billionaires who never received a full education, but even without those bits of paper society looks to them as pillars of success and attainment and often quizzes them looking for insights into their brilliance.

So, what is it? What decides if a person is smart, or an answer is valid? If you were to take an IQ test the metric changes again, there are no essays, no questions on what year the declaration of independence or magna carta was signed. You’re challenged to solve a variety of mathematical problems, logic questions and puzzles. The problem here, however, is that if my IQ is based on my ability to solve a puzzle, the insinuation being that a person is ‘smart’ if they are able to solve a problem.

But here’s the paradoxical question, if I know that a computer can solve a problem for me, by abstracting to the wider issue have I not just solved a problem? Think of it like this, if I don’t know how to answer it, but I do know a way to find a solution to this problem, is that not still an answer, I’ve used my recall to remember where I can find a solution, and my critical thinking around the best and most efficient way to solve the problem, knowing that a computer is far better than me at mathematics. So, what are we really asking? Is it the answer that is the most important part that decides my intellect or the way by which I got to that answer? In the world of business, we often are results-oriented using the means at our disposal to get to a solution for the problem in front of us. In a very oversimplified view that might suggest that really the results you achieve are the main and only useful indicator of your ability.

The same sorts of questions arise when we discuss the notion of creativity, what does it mean? To use the well-known Mark Twain expression ‘There is no such thing as a new idea’, although many might counter this, it raises the question, is an idea ever original? Whether that be in the arts or sciences or is it simply a manifestation of knowledge and influences that we have accumulated over our lives that we have then used to create our own thoughts and expressions.  

Now before I dive deeper down the rabbit whole of pseudo-philosophical discussion I will cut it short by simple pointing out that on both subjects, Intelligence and Creativity, there is no universally agreed definition, don’t get me wrong there are hundreds of ideas, proposals and stances on what they are but we as a species are yet to agree on their actual definition and when it comes to creativity in particular in historical terms it still is a relatively new concept.

So, why is this important? Well as many will have had dominating their newsfeeds, social media pages and probably work at this point in recent weeks and months, we are currently in the midst of discussion about the pre-eminence of Generative AI. With discussions ranging from its applications to its ethical and legal issues to the subject of this article, which is our fundamental concern over its ability to replace us as it causes us to question our very conception of what it means to be intelligent or creative.

What should be our stance when a technology can write better code than someone with a computer science degree? what should we think when it can create award winning pieces of art in a few clicks that beat human artists? For many, our immediate reaction turns to fear, fear of obsoletion, that the advent of a new technology will eliminate the need for our careers and livelihoods that we have spent decades incubating and developing. But should we fear it? 

Fear and Loathing in the Age of Generative AI 

Our media streams have been awash in the last few months with stories, stories that often do raise legitimate and serious questions but more often than not teeter on outright fearmongering about the technology. Will it wreak havoc on academia? will it completely eradicate the need for certain job sectors by automating and accelerating the work? and what does this mean for us as people from a livelihood and a development perspective?

The first thing to point out in this argument is that this alarmism over the narrative ‘The Technology is Coming to Take our Jobs’ is not new and more importantly is misplaced. If you look back technology has rarely directly displaced people from work and doesn’t necessitate the complete eradication of an industry or sector, what it does do is cause disruption and change, neither of which are necessarily negative, but despite how often we espouse our ability to adapt, people often simply don’t like change.

Let’s elaborate on this a bit, at the time it was a certainty that the creation of the camera was going to destroy the portrait painting industry in the 1800s, but it actually led to a resurgence. There are countless examples of the difference between our assumed negative impact of a technology and its often-counterfactual reality. We might have thought that the adoption and advancement of smartphones would detrimentally affect the photography industry, yet if anything it has boosted it with the possibility of creating new and engaging content. eBooks never ended up destroying the publishing industry and despite having quite literally nearly all of humanities knowledge and learning at our fingertips thanks to Tim Berners-Lee, the internet which I would argue is a more fundamentally impactful technology has not led to an erosion of the education sector and teachers’ jobs.

So why then is our narrative surrounding the increasing abilities of Generative AI littered with the assumption it is here to replace us? Well one theory I agree with is that until now there has been a generic assumption that those professions most likely to be replaced by technology are more likely to be the working class and blue-collar workers, not those of us sat in the sacred space of knowledge and creative work believing that because our professions require us to ‘reportedly’ think more and be more expressive that we were safe, because of course technology is amazing but it can’t do some of the processes that humans are specialised at.

Such assumptions were even encapsulated in a popular report published by Oxford University in 2013 that captured this conventional belief with regards to the professions most at risk from the advances of technology, those jobs most likely to be threatened by the ever-imminent wave of automation and artificial intelligence. And as one might assume these occupations were the blue-collar sector, those jobs doing repetitive, routine, ‘unimaginative’ work for which AI would make them an endangered species, able to outperform them and slowly move them into redundancy, whilst those in the tower of white-collar jobs would be safe from this professional encroachment. Well, it’s been a decade, and this still hasn’t happened.

Our outcry now however is because this outdated and always dubious wisdom and feeling of safety has been removed with the advent of the latest series of Generative AI technologies. Our tower is collapsing, and our sacred space is being invaded by the machines who suddenly can perform the work we’ve long held as only being possible by humans; music, art, literature, consulting….

I honestly don’t fully see the surprise in white-collar being affected before blue-collar, we built machines that embarrass us at chess long before we created cleaning robots that still can’t get to those annoying corners in my flat, a robot that I have to reset every ten minutes because it got stuck again.

Prior to now AI for many has been in the realm of the future, something for which we’ve heard about and how important it is but never truly engaged with, it’s what Billion Dollar companies and Computer Geniuses spend millions on developing and which operates in its own invisible space giving me Netflix recommendations or working out what TikTok video I’m going to enjoy next or getting Alexa or Siri to tell me how long it takes to boil an egg.

It’s been in our lives for years, influencing and helping us but has always been in the background and has always remained within our expectations of what a machine can do, hence Alexa refuses to answer me when I ask, ‘Why do people say I’m longwinded?’. Whether with Generative AI, it’s now moved to the foreground, and we’re concerned that rather than Siri helping us, her Generative AI cousin is about to start impacting us for the worse. 

Stop Copying Me: Is Generative AI About to Take My Job? 

So Generative AI is going to take our jobs and it’s going to do it as it is writing the next Shakespeare Play whilst simultaneously singing a Grammy Award winning song it created in less than a few minutes, or at least that’s the concern. There is no doubt that generative AI has begun to possess and express qualities that appear to be becoming more undeniably human and mimic cognitive functions we thought only possible by people, impossible to replicate. But now we’re wrestling with the prospect that a machine can create work equal in quality to ours and as a result we’re throwing every legal and ethical blockade we can to stop its advance.  

As is the subject of this article two of the industries currently grappling most with what Generative AI means for them are the creative and knowledge industries, the realms of artists, lawyers, musicians, doctors, filmmakers and… consultants. So, before I discuss the imminent demise of my career, I think we’ll tackle the creative industry first. 

By now I expect many of us have come across some examples of Generative AI art or perhaps have dived into experimenting with the likes of DALL-E and Midjourney, curious at what our series of prompts will lead the mystery machine to create and have sat there in amusement and wonderment as our creation comes to life in seconds. I equally expect many of us have begun to see the endless stream of news articles and stories surrounding all the ethical and legal issues that these systems are prompting, as they show themselves capable of replicating artistic and writing styles and even voices. As artists and creatives file lawsuits looking to prevent their ‘unique’ style from being copied and used to create digital paintings of a cat playing paintball. 

Don’t get me wrong, there are many legal and ethical issues that need to be thoroughly explored when it comes to Generative AI. Just the other week David Guetta made headlines with an original Eminem song during his concert, except, Eminem wasn’t involved at all, a Generative AI had been used to replicate the artists voice to use in the song. It’s as amazing as it is concerning and these ethical, socio and legal ramifications need to be unpacked as once again we struggle behind the rapid pace of our technological development (don’t worry a separate article is coming on that). 

But for now, we’re keeping on topic, and that is the concern that with a technology able to perfectly mimic and transpose someone’s style for someone else to use, there are natural concerns around whether this is going to affect a career that has taken a lifetime to master; that a machine is now recreating in seconds. And as a result, we’re regressing to hostile protection, demanding exclusion of our work from the training data that the algorithm runs on, stopping our names from being used to try and limit anyone taking what we think is ours. 

But for me, I circle back to one of our opening questions and that is, what is human creativity? Creativity as we understand it today is actually a fairly modern concept, its origins slowly forming during the renaissance where we delineated between the creation by man vs. the divine, but as an independent topic and concept of its own ‘creativity’ really never became a subject of study until the late 19th and early 20th Century with the actual term ‘creativity’ only coined in 1927 by Alfred North Whitehead. 

So, now we’ve established creativity is technically less than 100 years old, but what is it? That’s a great question, and if you have a correct answer the academic community would like to know. As to put it quite simply our agreement on what ‘creativity’ is, is non-existent. There have literally been studies simply exploring the number of divergent and varied definitions for which there are hundreds (Meusburger, 2009) and we still are no closer to a precise definition. Yes, we have ruminations about what it involves but nothing definitive. So how are we able to say whether something is infringing on said creativity. 

I know, this is all getting a bit meta, but let’s just take one idea, that creativity involves originality. That’s something we all agree on, right? That people might have an original style or produce something unique, and they rightfully don’t want that to be copied by a machine to influence its output? Seems fair enough, but was it right for them to copy others? 

To make this point, let’s start with a question, Who are some of the most famous artists to have lived? Your mind is probably going to go to a few options, Da Vinci, Van Gogh or the one I’m using for this point Pablo Picasso, who is attributed as saying this “good artists borrow, great artists steal.” So, one of the greatest artists in history renowned for their own creativity and style actively suggests stealing from others. 

Now do I think this is enlightened? No, because like creativity, the idea of originality is a modern invention that has been venerated as an ideal, but as an operational concept is questionable. Historically our focus on something being ‘original’ was not considered central to the quality of a piece of work and in many periods, there was great appreciation of similarity and recreation in the style of others. Even Shakespeare, the universal reference for a literary icon avoided ‘unnecessary invention’.

So, if both one of the greatest artists and one of the greatest writers had no qualms about imitation of others why are we so thoroughly opposed and objecting to generative AI using the styles of others to produce new work. I personally fall squarely into Twain’s camp, that with some conditions there is no such thing as an original idea, one reason is because we have occurrences of simultaneous invention as just one phenomenon that I think disproves the ownership of an ‘original’ idea.

But more than that, here’s why. We are a species of continuous learners, either actively or passively. We are constantly being influenced by others around us, and this is the same in the creative space. If you go on the Wikipedia page of most musicians for example at some point in time, they’ll have been asked who their greatest inspirations or influences are, the same goes for artists, filmmakers and writers. When they go on to produce their own work are they duplicating? No, but what they are doing is blending their collective knowledge and influences into something they produce, that retains elements of what’s influenced their personal creations. We then end somewhere in the middle, with something that is new but not entirely independent of the influence of others. 

The very same goes for this article, as I write it’s a worthwhile question, is it original? Technically, yes. But it’s equally a production influenced by years or reading other authors, that has ended with this overly explanatory and often chaotic writing style in which if I delved deep enough could probably find identifiable characteristics of others (probably a pre-1900s Russian author) and it is equally influenced by the dozen or more articles, I read that in different capacities spoke on the same subject, being inspired by some of their thoughts, taking them and elaborating, countering where I don’t agree etc. 

We need to extract ourselves away from this idea of self-exceptionalism, in the belief we are original in what we produce and return to an appreciation of the derivative. In recent times we’ve had countless issues and lawsuits and fines when it comes to humans making works that are similar but different to others yet with the advent of Generative AI this has been compounded with added animosity. My personal belief is that such an approach simply limits our collective creative potential, I don’t remember Van Gogh painting a goat reading the newspaper, but I’d love to see what it might look like. I believe that as long as a work is not a direct copy, we need to learn to appreciate that our styles are so affective on others that they want to see what else you could produce and see the realisation of their imagination meeting yours. 

The End of Expertise? Generative AI and the Future of Knowledge Workers

On to the important question, so is the age of knowledge workers about to meet its end and will my father get his wish about me learning a practical skill, like carpentry? 

There’s no delusion that for some time now, machines have been superior to us at analysis, they can take impossibly large amounts of data that we as people wouldn’t have time to read let alone analyse and it is able to identify and show us patterns, these help us do things like detect fraud or what TikTok viral dance video you will probably like next. This for much of its existence has been our expectation of AI, or traditional AI hence titled “Analytical AI”. 

This has provided an effective delineator for many of us, especially in the knowledge industry, between where the work of AI and the work we do diverge. AI supplying much of the cognitive labour and heavy lifting analysis work whilst we primarily focused on taking that analysis and converting it into insight and contextualising it, turning it into products and services to be used. From the doctor that takes the tests results to turn into an effective treatment plan or those like myself who takes the shared understanding of a technology and a specific business to construct strategies for improvement and development. 

However, with Generative AI, AI suddenly is free from its analytical confines, and is to some degree able to create recommendations based on surrounding context and information. This is a daunting prospect for many of us with the idea that if clients can simply ask an AI what an effective strategy may be, for which I have already seen attempts to do so, well then, what happens to our jobs? 

Now before I get into a long (and desperate) explanation for why I still should have a job, the short answer is that, although Generative AI has undoubtedly made amazing strides in both the knowledge and creative domains, it’s not at a point where it could replace us or imitate and understand the full complexity of a problem or human cognition and the even simpler explanation behind this is the reminder that a Generative AI is not ‘thinking’.  

So now that I’ve justified why you should keep paying me, does that mean there isn’t room for concern or that we’re going to be unaffected by Generative AI? This is a resounding no, although Generative AI won’t be kicking us out of a job anytime soon it does change the dynamics of what our jobs involve and escalation of what we define as ‘quality’ and ‘valuable’ and this relates to the areas mostly that Generative AI simply is not able to understand.

There is no question right now, that if you went to ChatGPT and requested it to provide an internationalisation strategy for your business it would provide a variety of strategy options and feeding it even more information may narrow down these options further. So where do ‘consultants’ like me come into play, when a machine that has all the answers can provide the options quicker (and yes undoubtedly cheaper)? 

Well, here is where I predict Generative AI will create an overhaul for the industry and many related knowledge industries, fundamentally changing the requirements of our job and our definition of “expertise”. If you’ve been in business circles you’ll have probably heard about the ‘T-model’, Tim Brown’s framework for describing the ideal skillset for knowledge workers that mixes a broad understanding with a deep expertise in a specific area. 

The reality is that although this holds true, for many in knowledge industries we’ve gotten by on what I would describe as a superficial T-Model where we do have a much deeper understanding than the average person but the title of ‘expert’ is debatable, it’s permanently predicated on the knowledge limitations of others and the notion that there was no available system that could easily provide an easy understanding of the work and strategies without substantial time invested in studying them. That is simply no longer the case, I can ask ChatGPT to explain it to me in simple terms. 

We can no longer get by in churning out generalist strategies that could theoretically be copy and pasted across multiple businesses and industries, especially when a Generative AI can provide many the same answer and observations. I suggest that the T-Model will stay the same but with different proportions, the future knowledge worker will need deeper T-models and no, that’s not a grammatical mistake, ‘models’, they will need multiple areas of deep expertise on the subject matter in order to keep up with and provide insight beyond Generative AI and thereby justification for their advice. 

Additionally, I mentioned that Generative AI still has limitations that prevent its replacement for human creativity, as inevitably the technology is only as good as the data it is trained upon. But still, as more and more data is fed into it and tailored GAIs are created based on companies own internal data this will improve. Where there is still the space for knowledge workers, is in the best utilisation of this augmented knowledge, generative AI still will not be able to understand the underlying context of an answer it provides, nor will it have other dimensions of intelligence i.e., Emotional Intelligence, and it will most importantly not have the spontaneity or intuition that allows human intelligence to make the leaps from an apple falling from a tree to an insight of an underlying scientific principle. 

Summarily, although Generative AI will improve and continue to amaze us it will not possess the human touch, the ability to fundamentally understand people and convert insight into actions and that is where knowledge workers will need to focus. On the critical abilities of being able to ask the right questions, get to the root cause of a problem, link the correct ideas and as paramount taking a people-first approach. To convert insights into actionable results, the profession will intensify its focus on value-driven outcomes and in understanding how to help people, companies and societies make the best use of these insights by utilising deep understanding of the technology, subject and most importantly people. 

I am frustrated that I can’t remember its source but remember an interview I once watched/read that asked the same question about technology as a whole that we’re asking now about generative AI. The interview focused on the belief I highlighted that technology was always to replace ‘unskilled’ repetitive workers and there was no real risk to the mathematicians and computer scientists. To which the expert gave an insightful opinion, that they believed AI would replace computer scientists and mathematicians long before it replaced psychologists and those with liberal degrees that sought to better understand people. That seems truer now than ever before. 

Beyond the Hype: Understanding the Real Risks of Generative AI

So, having conveniently established my job is not at risk, I think it’s important to have bit more understanding on the impact of Generative AI both on creative work, knowledge work and people. I stand by the idea that although Generative AI will not lead directly to the loss of professions or jobs, it will change the ways in which the job or activity is done and that it will result in our need to change our beliefs around some of these concepts i.e., intelligence or creativity. But that doesn’t mean I don’t believe there are not significant risks when it comes to GAI.

The biggest risks I believe relate to our personal development and learning. Although GAI looks to change the world of academia and education in many positive ways with the prospect of tailored learning or education that can adjust its explanation to the student, there is a downside and that comes in the form of our overdependence and its consequences.

I discussed the debate about what constitutes intelligence in my introduction and the current grappling educators and academics are having with the prospect of indistinguishable GAI generated essays and work, but what needs to be highlighted is the underlying concern that if students or people in general become overly reliant on an AI tool there are possibilities of significant and detrimental long-term consequences.  

One of these concerns relates to the lack of knowledge accumulation whereby simply getting the answer without understanding it lessens its retention or application and for all our discussion on creativity without an array of knowledge that informs the cognitive processes that inspire creativity our ideas will become more limited. The problem here is that it leads to multiple issues that raise the question of whether over-reliance on GAI will leave us on average less intelligent and knowledgeable. Because of the ability to get answers immediately without understanding them on a more fundamental level. Now this is fine if I’m just wanting to know what date the declaration of independence was signed, which a search engine can already provide, but more problematic if wanting to understand the underlying causes that led to independence.

Continuing this point of losing our general knowledge and acquisition comes the problem of what happens when a support tool is no longer available? Our use is predicated on its availability, which if removed raises practical concerns, say for example if a medical worker is not able to think for themselves in an emergency and didn’t acquire the long-term knowledge because a GAI was used to help formulate answers to tests. This overall risks compromising both our decision-making capabilities and logical reasoning of cause-and-effect relationships if only learning select elements of a subject rather than its wider reading.

At a deeper level the use of Generative AI and a future in which people are reliant on it brings about a more primal concern, which is that despite our reverence of creativity and imagination a dependency could lead to the loss of critical and creative thinking skills, which should become more, not less important, as Generative AI becomes more pervasive throughout society. By simply relying on GAI and the information it provides to a question limits wider awareness and consideration on the topic, this lack of awareness and knowledge limits the possibility for our imaginations and creativity to spring new ideas through association and further lessons our ability to naturally innovate.

Furthermore, relying solely upon the answers of GAI as fact is both limiting and potentially highly inaccurate. This leading to dangerous outcomes ranging from simply providing a wrong answer to introducing and reinforcing several biases into research, outputs and therefore decision-making. As simply put GAI’s such as ChatGPT are language models programmed to provide responses that are intended to be helpful and relevant to the user’s query, however, it cannot independently verify the accuracy of the information it provides (although this will be likely subject to change in the near future) and relies heavily on the quality of the input it receives.

This input is vital as it’s what leads to misleading or false information that could be mistaken as factual. As we’ve already seen many people experimenting with ChatGPT (hopefully comedic intentions). The issue is that although these instances are used to poke fun at the holes in these models, if you were a young child or completely new to a topic and weren’t aware these responses are either satirical or just inaccurate, the explanations these LLMs provide often are fairly convincing in terms of their conviction with no mention of the confidence in its accuracy. Now, there are disclaimers these models often provide i.e., encouraging users to exercise their own critical thinking skills, this is indeed important but as a reader I would ask, how actively do you think critically, when given a figure or something suggested confidently as fact do you always verify it with multiple sources? Do you check the reliability of a source, or its relevance based on how long ago it was published?

I can say I certainly don’t, as often we’re looking for quick answers, resolutions to our immediate and time limited problems which until now was problematic but not perilous for the majority of topics and thanks to most engines having algorithms in place that often prioritised more reliable sources in results. But a society that propagates reliance on these models without educating about their limitations or risks could lead to a spiralling series of problems. One of which is the inundation of information overload, with these tools able to produce entire essays in seconds that can then be used and shared which saturates our learning environments and sources with more and more information, and further blurs the lines of what is a reliable and accurate source vs. what is not.

The final risk that I think has been overlooked is probably in my opinion one of the most insidious, and that is an ever widening of the digital divide, both intra and internationally. Now according to a study by Accenture in 2017, research shows that AI has the potential to boost rates of profitability by an average of 38 percent by 2035 and lead to an economic boost of US$14 trillion across 16 industries in 12 economies. This not far removed from a similar study by our good friends at McKinsey who also in 2017 suggested AI could contribute up to $15.7 trillion to the global economy by 2030. So, more money for everyone! What’s the problem?

Well, that’s exactly it, the distribution of this value to the global economy is by no measure equitable and risks widening the multiple divides between the north and south, with the World Economic Forum saying as much in a January Article that highlighted:

“The economic and social benefits of AI remain geographically concentrated, primarily in the Global North. Without an enabling operating environment, disparities in AI readiness will feed into global inequality. There is tremendous potential in harnessing the power of AI tools to increase economic growth, but the economic and social benefits of this technology remain geographically concentrated, primarily in the Global North.”

Besides the social and economic inequalities such divides will continue to reinforce, sticking with the subject of this article one major concern from a learning and knowledge perspective is that with Generative AI providing an increase in the velocity of information being generated by the Global North because of the aforementioned technology and AI divide, this will risk increasing and perpetuating the domination of the North in Academic Knowledge Production, whilst intensifying the under-representation and marginalisation of the Global South.

Unless you have studied specific topics this idea of narrative and knowledge hegemony is often a socially blind issue with detrimental implications to both the oppressed social groups as well as all society as we see a field that remains equally divided by North vs. South and West vs. Rest divisions. I wanted to try and frame this in my own way but could not think of a better expression than Martin Demeter’s on the very subject.

“These cultural, epistemic and rhetorical differences between geopolitical locations do not result in a diverse, inclusive and pluralistic global academic field, but instead help to develop an elitist, Westernized world-systemic structure of knowledge production. In this transnational academy, central agents have substantive control over the whole system, and through this control, they systemically exclude the non-elite social classes and the geopolitical periphery from global knowledge production. This phenomenon is labelled by many names, such as knowledge colonization (Mignolo 2011), academic imperialism (Mignolo 2018) or simply knowledge hegemony and exploitation (de Santos 2018).”

My great concern is that if not properly managed and protected against, which all evidence to date suggests will recur as it has already, we will see a world in which the North and West are able to amplify their production and domination of academic and social discourse through the use of Generative AI increasing the marginalisation of other groups and excludes valuable and insightful voices that we need to hear in a world that will require far more focus on people than technology. 

The 21st Century: The Hope of a New Renaissance

As we look to close our ruminations on the impact of Generative AI on our understanding of Knowledge and Creativity I’m reminded that we need to address one of the most important elements of our discussion and that’s the subject of my title, and why despite everything we should be immeasurably excited for the future and what the use of this technology could bring as the thought of which brings to mind a quote from a personal inspiration of my own Walter Disney and that is, “If you can dream it, you can do it”. 

Varying across applications we are either slowly or rapidly entering a period where the limits of our progress are soon to be removed, with a convergence of emerging technologies on the cusp of commercialisation as significant breakthroughs continue to happen year upon year. All of which individually are extraordinary but collectively will be revolutionary and, with the condition of their responsible application and safeguarding, could in my view could look to usher in a new age renaissance. 

Beyond this confluence of technologies Generative AI if utilised correctly offers one of the most exciting developments to this future, heralding a revived period of cultural, artistic, economic, scientific, and intellectual growth and advancement. GAI holds the prospect of opening new worlds of ideas and possibilities, with its ability to massively increase labour productivity and economic value by reducing the marginal cost of creative and knowledge work through automating routine but time-consuming tasks in the creative process, providing insights and inspiration, personalizing content and experiences, and making knowledge and creativity more accessible and inclusive.

That final point is one that I believe deserves special attention, GAI holds the possibility of opening up opportunities in creative and knowledge work for everyone. For decades the creative and knowledge industries have been exclusionary, reserved for the ‘educated’ or those with some apparently innate talent and guarded by the barriers to entry of using of the tools of the trade, the idea that not everyone is a natural artist, and the fact that not everyone has the resources or opportunities. 

But GAI has the potential to change this, creativity and imagination are not the realm of the ‘special’ amongst us, it is for everyone. I’m sure at some point in your life, there’s been moments, where you’ve had an idea, a moment of inspiration where your imagination propelled an image or thought to your mind, an idea so compelling that you desperately wished to create it. But alas, that idea became lost because you’re not a skilled painter, or musician, you don’t have a degree, or maybe you just don’t have the resources or time. GAI changes that, it offers the chance to bring ideas to life and make what seemed impossible accessible, no longer are you limited by skills or time, they help but they are no longer a barrier. 

For a long time, there has been a view that technology inherently increases inequality and helps cement them. Part of this has been through the escalation of requirements, so in order to keep up with the technological development everyone’s skillsets have had to increase in tandem leading to increasing exclusion until only the few remained that retained access and ability to reap the benefits. Generative AI holds the potentially to drastically change that system, no longer is programming skills a pre-requisite to develop an application and no longer is expertise in applications like Photoshop required to create fantastic digital art or photography. 

The reason for my own excitement is spurred by another favourite quote of mine, one from Les Brown and that says:

“The graveyard is the richest place on earth, because it is here that you will find all the hopes and dreams that were never fulfilled, the books that were never written, the songs that were never sung, the inventions that were never shared, the cures that were never discovered, all because someone was too afraid to take that first step, keep with the problem, or determined to carry out their dream.”

With the democratisation of knowledge and creativity offers the opportunity to change this and ensure that ideas and innovations are not lost simply because of financial, time and skills limitations, ideas that can then contribute towards our collective development. 

However, despite my optimistic viewpoint on what GAI could lead to we need to clarify what are the enablers and rules that will make or break this somewhat utopian egalitarian vision. 

Enabling the Future Relationship with Generative AI 

1.Breaking Barriers: Why Access to AI is Crucial for Everyone

As Generative AI continues to advance, there is a growing concern that access to the technology will become increasingly stratified. This is a real danger, as those who lack access to Generative AI will be at a significant disadvantage in fields ranging from medicine to music. To prevent a widening digital divide, it is essential that Generative AI tools and software be made available to everyone. Open-source platforms and tools, which can be freely accessed and modified, are a critical step in this direction. By making Generative AI accessible to all, we can unleash the full potential of the technology, and ensure that everyone has an opportunity to participate in the benefits it offers. This means investing in education and training, as well as fostering collaboration and open communication across different sectors and communities. Ultimately, preventing a digital divide requires a collective effort, and it is up to all of us to make it happen. By embracing open-source Generative AI tools and platforms, we can ensure that the technology is used in a way that benefits everyone, and not just a select few.

2. Beyond Accuracy: The Importance of Ethics in Generative AI Development

As probably the most discussed topic when it comes to Generative AI it remains ever clear that the responsible development and use of this technology must be a top priority. One critical issue that demands our attention is the risk of data bias, as algorithms are only as unbiased as the data they are trained on. As a result, it is essential that we work to identify and address bias in datasets to ensure that the models produced by Generative AI are fair and equitable. This starts and ends with data quality ensuring that all data utilised for its development and training abides by our common dimensions of data quality.

Additionally, although far too expansive to be addressed meaningfully in this article, ethical considerations must be at the forefront of our minds as we develop and deploy this technology, with a focus on issues such as data privacy, ownership, transparency, and accountability.

In order to achieve this, we need to bring together individuals from a range of backgrounds and experiences in order to help identify and address bias, ensuring that the models produced by Generative AI are fair and equitable. Collaboration between researchers, technologists, policymakers, and civil society is crucial for identifying challenges, opportunities, and ethical considerations, and to ensure that the development of Generative AI is proactive and responsible. Ultimately, by prioritizing diversity and collaboration, we can build a future for Generative AI that reflects the needs and values of all and benefits society as a whole, rather than perpetuating existing inequalities or creating new ones.

3. Knowledge is Power: Why Education and Awareness are Critical in the Age of AI

In order realize the many benefits and prevent the multiple risks of Generative AI, education and public engagement are critical. This means investing in initiatives that promote awareness and understanding of the technology, as well as fostering collaboration between stakeholders in different sectors. By doing so, we can ensure that Generative AI is developed and used in a way that maximizes its potential to improve our lives, while also minimizing the risks that come with it. The responsible use of Generative AI will require ongoing dialogue, research, and education, and it is up to all of us to make that happen, we will require a fundamental change in education from teaching to testing and educating both about the most effective usage of GAIs as well as its ethical usage which will need to form a key components for our future education.

4. Setting the Standard: Why Quality Control is Critical for Generative AI

With Generative AIs continued advancement at unprecedented pace, it is becoming increasingly clear that we must raise the bar for what we consider high-quality output. With the ability to generate vast amounts of text, images, and even music, the potential for both creative and destructive use of Generative AI is significant. To ensure accuracy, reliability, and relevance in what is produced by Generative AI, we must set clear standards and develop rigorous evaluation methods to measure the quality of models and the work produced by them.

Additionally, in a world burdened by an ever-increasing amount of information we must establish guidelines for publishing and disseminating information that is generated by utilising these models to ensure that it is trustworthy and credible. We already face issues regarding misinformation, propaganda and similarly duplicitous uses of technology and information by negative actors; a serious risk is in that through GAI models the rate of this contents production can increase exponentially. Hence defining clear standards, checks and balances to information dissemination is a requirement to prevent information overload and the dilution of quality and trust in publicly available information and news that could result from the unchecked use of Generative AI.

5. From Competitor to Collaborator: The Role of Generative AI in Your Work

To truly harness the potential of Generative AI, it is vital that we shift away from historical narratives and begin to think of it as a partner rather than a competitor. This means understanding the strengths and limitations of the technology and using it to complement and amplify our own skills and ideas. For example, a visual artist might use Generative AI to explore new forms and styles, while an academic or scientist might use it to generate and analyse vast amounts of data, or alternatively can utilise it as a perpetual devil’s advocate, generating alternative arguments or perspectives that challenge their assumptions and push their thinking to new heights. By exploring multiple angles and possibilities, Generative AI can help academics to approach their research from fresh angles and achieve breakthroughs that might not have been possible otherwise and continually refine our work and thinking.

By incorporating Generative AI into our work in this way, we can unlock new possibilities and push the boundaries of what is possible. Ultimately, embracing Generative AI as a partner requires a mindset of curiosity, experimentation, and collaboration. It means approaching the technology with an open mind and being willing to explore new ideas and approaches that might have been impossible without it. We have already seen the astronomical rise in GAIs usage, absolving yourself from using it will only result in becoming obsolete and disadvantaged, we need to ensure we are responsibly using it to maintain relevancy and competitive advantage.

6. New Skills for a New Age: The Role of Soft Skills in the Future of Work

Generative AI will see a rapid reshaping of the skillsets required for success in the workforce of the future. While technological expertise has always been highly valued, it is becoming increasingly clear that the ability to blend technical skill with creative and critical thinking will be essential for those who wish to thrive in the years to come, no longer an occasional but permanent way of thinking. As AI takes on more routine tasks, professionals will need to be able to think beyond rote solutions and find new and innovative ways to approach problems. At the same time, understanding human behaviour and motivations will be essential, as Generative AI is increasingly used, a more nuanced understanding of people will be essential in order to create products and services that differentiate from the collective who are equally using the same tools. In short, the workforce of the future will require professionals who can bridge the gap between technology and the human touch, bringing the best of both worlds to bear on the challenges of tomorrow, this is what will set workers in knowledge, creative and wider industries apart.

7. The Future of Work: Will Generative AI Intensify or Streamline Our Workload?

Now for one moment let’s talk theoretically, Generative AI holds the hope of increasing productivity by an order or magnitude equivocal to the adoption of the internet itself, allowing us to work, better, faster, and cheaper across a massive range of industries and markets particularly applicable but not exclusive to the knowledge and creative industries. That means the possibility of streamlining our work and moving closer to the future envisioned by one of the fathers of economics John Maynard Keynes in 1930 when he predicted a 15-hour workweek. A possibility that for many would be a positive-step towards improving wellbeing, reducing burnout and helping with work-life balance and fulfilment.

Now, a bit counterintuitively as much as I am an optimist, I’m also a pessimist, even with the massive increase in productivity I have doubts even the most forward-looking companies like my own would buy-in to the 15-hour workweek (as much as I might try to convince them), at least not without a comparative salary reduction which would negate any benefit. The more likely result will be the intensification of work, with GAIs handling large amounts of capacity in order for many to focus on ‘higher-order’ work. The expectation likely being to increase growth, profitability and all the traditional business metrics of success. However, the question needs to be asked, should it? what amount is enough? and how do we balance the desire for growth with the wellbeing of people?

The impact of Generative AI on the workforce is not limited to changes in required skillsets - it also demands a new approach to the type of work that people do and the expectations that both employers and employees bring to the table. As technology increasingly take on routine tasks, professionals will need to focus on work that adds value, whether that be through creativity, problem-solving, or other higher-order tasks that cannot be replicated by AI. Employers will need to shift their expectations about the work that people do, rewarding creativity and innovation over the ability to complete a task quickly and efficiently and understand the taxation of such continuous mentally intensive work. Meanwhile, employees will need to approach work with an eye towards adding value and making a meaningful impact, rather than simply completing a checklist of tasks. In short, the rise of Generative AI requires a fundamental shift in how we view work and what we expect from it - a shift that will demand new approaches to management, training, and professional development in the years to come.

Finding the Balance: The Intersection of Innovation and Responsibility in Generative AI

As we wrap on our final point, lets remind ourselves of the wisdom of Spiderman’s Uncle Ben “With Great Power, Comes Great Responsibility”. To mention a point that is too expansive and important to encapsulate sufficiently here, the future trajectory of Generative AI is entirely dependent on its regulation and governance.

Generative AI and how people are using it is advancing and changing every day as people become more curious and see more possibilities for its application, it has the potential to transform countless areas of society and business, from healthcare to finance to entertainment. But as with any powerful tool, there are also risks and challenges that must be addressed, as our regulatory and legal systems once again play catch-up to our technological development.

Regulations are necessary to ensure that Generative AI is developed and used ethically and responsibly, protecting the rights and wellbeing of individuals and society as a whole. However, there is also a need to carefully consider the balance between creating laws, rules, and regulations that balance the wellbeing of people without hampering technological progress, development, and innovation.

This means working with policymakers and other stakeholders to develop regulations and standards that protect the public interest while fostering innovation. We as individuals, governments, industry and society need to take a proactive, balanced approach, to ensure that Generative AI is a force for good, that benefits societies progress whilst protecting our most fundamental values and rights.

Final Thoughts (…for now)

As we conclude, I leave you with a reminder about the impact of Generative AI and really technology as a whole. Like any tool GAI is not inherently good nor bad, its impact and effects are entirely dependent upon how it is used by people. We often tend to anthropomorphise tools and technology, we call it Alexa or Siri, and start to associate them with having intentions of their own, but it simply is a human habit that negatively influences people’s opinion of the invention. Remember, our discovery of how to split the atom led to both devastating nuclear weapons and life-sustaining nuclear energy. The insinuation that a technology is good or bad is a false dichotomy that holds us back and prevents adoption.

Generative AI is indeed an extraordinary development, that will undoubtedly impact and change jobs and industries, but nothing more than has happened multiple times over the last few decades, a reminder that the internet, probably the most fundamental revolution of human knowledge and information since Gutenberg released the printing press in 1454 only came out 30 years ago. The iPhone which changed how, when, why and what we utilise our phones for came out just over 15 years ago in 2007. Both inventions fundamentally changed the way we live and work, yet if I told you they were dangerous, or we should stop utilising them your average person would think you’re mad. We live in an age and state of constant change and adaptation and in the case of Generative AI for this too, we will adapt. 

Reference & Sources

1. Andriole, S. (2023, January 3). How Generative AI Will Change Business – All You Have to Do Is Ask. Forbes. https://www.forbes.com/sites/steveandriole/2023/01/03/how-generative-ai-will-change-business--all-you-have-to-do-is-ask/?sh=22dc8b1466df
2. Davenport, T.H., & Mittal, N. (2022, November). How Generative AI Is Changing Creative Work. Harvard Business Review. https://hbr.org/2022/11/how-generative-ai-is-changing-creative-work
3. Sequoia Capital. (2022, October 20). Generative AI: A Creative New World. https://www.sequoiacap.com/article/generative-ai-a-creative-new-world/
4. Thompson, D. (2022). Your Creativity Won’t Save Your Job From AI. [online] The Atlantic. Available at: https://www.theatlantic.com/newsletters/archive/2022/12/why-the-rise-of-ai-is-the-most-important-story-of-the-year/672308/.
5. Cunff, A.-L.L. (2022). AI and I: The Age of Artificial Creativity. [online] Ness Labs. Available at: https://nesslabs.com/artificial-creativity#:~:text=Artificial%20creativity%20is%20a%20new [Accessed 20 Feb. 2023].
6. The Media Line. (2023). Generative AI Is Revolutionizing Content Creativity, but at What Cost? [online] Available at: https://themedialine.org/life-lines/generative-artificial-intelligence-could-change-our-world-israeli-tech-leaders-say/
7. Unmistakeable Creative, (2023). Why AI Will Have a Positive Impact on Creative Work. [online] Available at: https://unmistakablecreative.com/ai-positive-impact-on-creative-work/#:~:text=AI%20will%20have%20a%20positive%20impact%20on%20creative%20work%20by
8.  Muhammed, R. (2023). The Dawn of Generative AI: A Threat to Creatives or a Boon? [online] Available at: https://www.wowmakers.com/blog/generative-ai/
9. Kelly, K. (2022). Picture Limitless Creativity at Your Fingertips. Wired. [online] 17 Nov. Available at: https://www.wired.com/story/picture-limitless-creativity-ai-image-generators/.
10. Mayahi, S. and Vidrih, M. (n.d.). The Impact of Generative AI on the Future of Visual Content Marketing. [online] Available at: https://arxiv.org/ftp/arxiv/papers/2211/2211.12660.pdf
11. Wu, Z., Ji, D., Yu, K., Zeng, X., Wu, D. and Shidujaman, M. (2021). AI Creativity and the Human-AI Co-creation Model. Human-Computer Interaction. Theory, Methods and Tools, pp.171–190. doi:https://doi.org/10.1007/978-3-030-78462-1_13.
12. Davies, J., Klinger, J., Mateos-Garcia, J. and Stathoulopoulos, K., 2020. The art in the artificial AI and the creative industries. Creat Ind Policy Evid Centre, pp.1-38.
13. Gottfredson, Linda S. (1997). "Mainstream Science on Intelligence (editorial)" (PDF). Intelligence. 24: 13–23. doi:10.1016/s0160-2896(97)90011-8. ISSN 0160-2896. Archived (PDF) from the original on 22 December 2014.
14.  Nunes, A. (2021). Automation Doesn’t Just Create or Destroy Jobs — It Transforms Them. [online] Harvard Business Review. Available at: https://hbr.org/2021/11/automation-doesnt-just-create-or-destroy-jobs-it-transforms-them.
15.  World Economic Forum. (2003). The ‘AI divide’ between the Global North and Global South. [online] Available at: https://www.weforum.org/agenda/2023/01/davos23-ai-divide-global-north-global-south/#:~:text=While%20all%20regions%20of%20the
16. Demeter, M., 2020. Academic knowledge production and the global south: Questioning inequality and under-representation. London: Palgrave Macmillan.
17. The Economist. (2019). The value of freeing ideas, not just locking them up. [online] Available at: https://www.economist.com/open-future/2019/11/08/the-value-of-freeing-ideas-not-just-locking-them-up
18. Haponik, A. (2023). Addepto. [online] Addepto. Available at: https://addepto.com/blog/what-is-generative-ai-and-will-it-replace-human-creativity/#:~:text=Generative%20AI%20is%20a%20powerful