A chatGPT,generative AI an the lack of innovation in the physical realm.
"Imagination is more important than knowledge." - Albert Einstein
In today's rapidly advancing technological landscape, artificial intelligence (AI) has become a transformative force, revolutionizing various industries. Generative AI, in particular, has garnered significant attention for its ability to create novel content in the virtual realm, such as art, music, and text. However, as generative AI continues to impress with its creative capabilities, concerns have arisen regarding its impact on innovation in the physical realm. This essay delves into the paradox of generative AI, highlighting its limitations in fostering genuine innovation in the physical world.
The AI revolution is here. Generative AI products across audio, video, image, and text are being developed at an astonishing pace. The current AI revolution marks an important milestone in humanity’s unrelenting march in the digital realm. First came the Internet, then the smartphone, and now AI. I am convinced that developing an Artificial General Intelligence that has human-like cognitive abilities to perform a wide range of tasks across different domains is only a matter of time.
We’ve had AI and machine-learning-based tools in our software products for some time, but the current set of AI tools feel radically different. These AI tools such as ChatGPT are so intelligent, contextual, and advanced that simple text-based prompts can output complex code, original poems, and captivating stories. These tools can also comprehend intricate text, skillfully combine different concepts, and produce ingenious ideas. No wonder that ChatGPT has become the fastest and the most widely adopted product in a record time. Many sectors— education, health, research, academia, and government — are ripe for disruption. The possibilities from here on are endless.
When was the last time such a breakthrough innovation happened in the physical realm?
I recently came across a social media post that showed a giant poster wrapped around an unfinished building. The ad sarcastically quips, “Hey ChatGPT, finish this building…” pointing to the inadequacy of ChatGPT to do physical tasks.
It made me wonder as to why the pace and scale of innovation in infrastructure and industry is nowhere near the speed of digital innovation?
Consider cement concrete, one of the foundational pillars of modern civilization. Cement concrete is a mixture of Portland cement, water, sand, and gravel. It is durable, sturdy, can withstand wide temperature and pressure fluctuations. It is also incredibly resistant to wind and water erosion. Because of these advantages, concrete is used everywhere: bridges, dams, houses, hospitals, roads, and all the towering skyscrapers.
However, for all of its advantages, constructing buildings and paving roads with concrete is tedious. Concrete is not good at tensile stress, so it must be reinforced with iron bars, which warrants huge labour. And then, you need to let concrete settle for a considerable time to strengthen. It may be sturdy, but it takes a ridiculous amount of time to build anything with concrete.
So it’s unbelievable to think that despite being invented over 2000 years ago, the basic formula and process for making concrete remain unchanged, with few truly disruptive improvements. The majority of the global infrastructure is built on cement concrete, which is known to have a significant environmental footprint, accounting for around 8% of the world’s carbon dioxide (CO2) emissions. Innovations have been made to reduce this environmental impact, such as “green” or “sustainable” concrete, but these are not yet in widespread use and do not essentially change the nature of concrete itself. The fundamental use and production of concrete remain similar to what it was hundreds, if not thousands of years ago.
Why haven’t we discovered an alternative to cement concrete that’s cheaper, more durable, and far easier to operate? Why isn’t there a disruptive innovation in infrastructure?
Prefabricated building technology was one improvisation that significantly reduced the time to build a structure. But we don’t see it around us because the technology is prohibitively expensive. And the far-fetched idea of printing buildings on site at scale is still a fantasy. In the age of digital acceleration, we are living a tale of physical stagnation.
Even roads too. The Covid pandemic proved that we can only go so far with remote work. People have a fundamental need to travel, and we need to pave thousands and thousands of kilometres of roads to sustain our growing population. How are we going to do it with asphalt and concrete? They are getting expensive by the day and just aren’t good enough for a 21st-century world. Why isn’t there a cheaper and faster way to lay roads and fix our potholes?
Even for our energy needs, we rely heavily on fossil fuels. It will be many decades before we reduce our dependence on coal, oil and natural gas. Why haven’t we solved nuclear fusion that can provide abundant energy for thousands of years to come? Even with nuclear fission, all the world’s nuclear reactors use designs that were created decades ago. More advance technologies such as Dyson spheres and space travel are still confined to sci-fi novels.
We live in a world where smartphones replace libraries, where artificial intelligence redefines work, and where the Internet connects us in an instant. Yet, we also live in a world where cement is still cement, engines still spit out smoke, and electricity is generated as it was generations ago.
We need to innovate and radically improve these processes. To ensure sufficient food for everyone, solve climate change, fix our energy security, and construct roads and buildings rapidly, we need disruptive innovation across these sectors too.
Despite the technological progress in software and smartphones, we have
stagnated in the physical realm. We are able to manipulate binary bits, not physical atoms.
It’s not just about a lack of innovation. Even with existing processes, over time, you expect efficiencies to come in and things to become cheaper. But the costs of building things in the physical world are becoming expensive, hindering rapid progress.
So it’s not enough to discover new materials and new processes; we need to make them cheap for them to be deployed on a mass scale. Like how ChatGPT upended workflows across every conceivable sector, we need innovation that makes paving roads, buildings, and high-speed rails cheaper, faster and lasts a long time.
Undoubtedly, these tasks present significant challenges; otherwise, we would have already accomplished them. But what’s worrying is we have stagnated so much that there is not much progress in these sectors. Our best minds are going for computer science engineering. We need them to work on industrial engineering, too.
Some hard problems in the physical realm that we need to urgently solve:
1. How can we build mega infrastructure projects at a rapid speed and scale?
2. How can we make trees grow 10x as fast without disrupting the ecological balance?
3. How do we harness Nuclear fusion to create abundant energy from atoms?
4. Can we discover a cheaper, faster, more durable alternative to concrete and asphalt?
5. Flying cars and personal aircrafts?
Now these are not forbidden by any laws of physics. So it’s a matter of us developing the knowledge and capability to make it possible.
Of course, there is hope! When the Covid pandemic shook the world, our science & tech community developed vaccines at a breathtaking pace. mRNA vaccines, which used to be confined to esoteric academic journals, became a reality. We built all these vaccines and delivered them at scale. That’s a truly disruptive innovation in the health sector. With the arrival of ChatGPT, we now have the intelligence to help us in the physical realm. Maybe it will help us speed up our physical innovation trajectory.
Here I am adding McKinsey report on state of AI few observation of their organisations.
Three Possible Futures
An explosion of AI-assisted innovation
Today, most businesses recognize the importance of adopting AI to promote the efficiency and performance of its human workforce. For example, AI is being used to augment health care professionals’ job performance in high-stakes work, advising physicians during surgery and using it as a tool in cancer screenings. It’s also being used in customer service, a lower-stakes context. And robotics is used to make warehouses run with greater speed and reliability, as well as reducing costs.
With the arrival of generative AI, we’re seeing experiments with augmentation in more creative work. Not quite two years ago, Github introduced Github Copilot, an AI “pair programmer” that aids the human writing code. More recently, designers, filmmakers, and advertising execs have started using image generators such as DALL-E 2. These tools don’t require users to be very tech savvy. In fact, most of these applications are so easy to use that even children with elementary-level verbal skills can use them to create content right now. Pretty much everyone can make use of them.
This scenario isn’t (necessarily) a threat to people who do creative work. Rather than putting many creators out of work, AI will support humans to do the work they already perform, but simply allowing them to do it with greater speed and efficiency. In this scenario, productivity would rise, as reliance on generative AI tools that use natural language reduces the time and effort required to come up with new ideas or pieces of text. Of course, humans will still have to devote time to possibly correct and edit the newly generated information, but, overall, creative projects should be able to move forward more quickly.
We can already glimpse what such future holds: With reduced barriers to entry, we can expect many more people to engage in creative work. Github’s Copilot doesn’t replace the human writing code, but it does make coding easier for novices, as they can rely on the knowledge embedded within the model and vast reams of data rather than having to learn everything from scratch themselves. If more people learn “prompt engineering” — the skill of asking the machine the right questions — AI will be able to produce very relevant and meaningful content that humans will only need to edit somewhat before they can put it to use. This higher level of efficiency can be facilitated by having people speak instructions to a computer, via advanced voice-to-text algorithms, which will then be interpreted and executed by an AI like ChatGPT.
The ability to quickly retrieve, contextualize, and easily interpret knowledge may be the most powerful business application of large-language models. A natural language interface combined with a powerful AI algorithm will help humans in coming up more quickly with a larger number of ideas and solutions that they subsequently can experiment with to eventually reveal more and better creative output. Overall, this scenario paints a world of faster innovation where machine augmented human creativity will enable mainly rapid iteration.
Machines monopolize creativity
A second possible scenario is that unfair algorithmic competition and inadequate governance leads to the crowding out of authentic human creativity. Here, human writers, producers, and creators are drowned out by a tsunami of algorithmically generated content, with some talented creators even opting out of the market. If that would happen, then an important question that we need to address is: How will we generate new ideas?
A nascent version of this scenario might already be happening. For example, recent lawsuits against prominent generative AI platforms allege copyright infringement on a massive scale. What makes this issue even more fraught is that intellectual-property laws have not caught up with the technological progress made in the field of AI research. It’s quite possible that governments will spend decades fighting over how to balance incentives for technical innovation while retaining incentives for authentic human creation — a route that would be a terrific loss for human creativity.
In this scenario, generative AI significantly changes the incentive structure for creators, and raises risks for businesses and society. If cheaply made generative AI undercuts authentic human content, there’s a real risk that innovation will slow down over time as humans make less and less new art and content. Creators are already in intense competition for human attention spans, and this kind of competition — and pressure — will only rise further if there is unlimited content on demand. Extreme content abundance, far beyond what we’ve seen with any digital disruption to date, will inundate us with noise, and we’ll need to find new techniques and strategies to manage the deluge.
This scenario could also mean fundamental changes to what content creation looks like. If production costs fall close to nothing, that opens up the possibility of reaching specific — and often less included — audiences through extreme personalization and versioning. In fact, we expect the pressure to personalize to go up fast as generative AI carries such great potential to satisfy the need to create content that is increasingly representative of the specific consumer. As a case in point, Buzzfeed recently announced it will personalize their content such as quizzes and tailor-made rom-com pitches with OpenAI’s tools. (They don’t plan to use generative AI in their newsroom, however.)
If the practice of enhanced personalized experiences is applied broadly, then we run the risk to lose the shared experience of watching the same film, reading the same book, and consuming the same news. In that case, it will be easier to create politically divisive viral content, and significant volumes of mis/disinformation, as the average quality of content declines alongside the share of authentic human content. Both would likely worsen filter bubble effects.
Yet even in this relative dystopia, there remains a significant role for humans to make recommendations of existing content in this ecosystem. As in other very large content markets, like music streaming services, curation will become more valuable relative to creation as search costs rise. At the same time, however, high search costs will lock-in existing artists at the expense of new ones, concentrate and bifurcate the market. This will then result in a small handful of established artists dominating the market with a long tail of creators retaining minimal market share.
“Human-made” commands a premium.
The third potential scenario that we could see develop is one where the “techlash” resumes with a focus against algorithmically generated content. One plausible effect of being inundated with synthetic creative outputs is that people will begin to value authentic creativity more again and may be willing to pay a premium for it. While generative models demonstrate remarkable and sometimes emergent capabilities, they suffer from problems with accuracy, frequently producing text that sounds legitimate but is riddled with factual errors and erroneous logic. For obvious reasons, humans might demand greater accuracy from their content providers, and therefore may start to rely more on trusted human sources rather than machine-generated information.
In this scenario, humans maintain a competitive advantage against algorithmic competition. The uniqueness of human creativity including awareness of social and cultural context, both across borders and through time will become important leverage. Culture changes much more quickly than generative algorithms can be trained, so humans maintain a dynamism that algorithms cannot compete against. In fact, it is likely that humans should retain the ability to make significant leaps of creativity, even if algorithmic capabilities improve incrementally.
In the development of this scenario, it follows that political leadership taking action to strengthen governance of information spaces will be needed to deal with the downside risks that could emerge. For instance, content moderation needs are likely to explode as information platforms are overwhelmed with false or misleading content, and therefore require human intervention and carefully designed governance frameworks to counter.
How to Prepare for Generative AI
Creativity has always been a critical pre-requisite to any company’s innovation process and hence competitiveness. Not too long ago, the business of creativity was a uniquely human endeavor. However, as we illustrate, with the arrival of generative AI, this is all about to change. So, to be prepared, we need to understand the accompanying threats and challenges. Once we understand what is to change and how, we can prepare for a future where the creativity business will be a function of human–machine collaborations. Below, we provide three recommendations that workers should consider as they adopt generative AI to create business value and profit in today’s creative industries.
Prepare for disruption, and not only to your job. Generative AI could be the biggest change in the cost structure of information production since the creation of the printing press in 1439. The centuries that followed featured rapid innovation, socio-political volatility, and economic disruption across a swathe of industries as the cost of acquiring knowledge and information fell precipitously. We are in the very early stages of the generative AI revolution. We expect the near future therefore to be more volatile than the recent past.
Invest in your ontology. Codifying, digitizing, and structuring the knowledge you create will be a critical value driver in the decades to come. Generative AI and large language models enable knowledge and skills to transmit more easily across teams and business units, accelerating learning and innovation.
Get comfortable talking to AI. As AI becomes a partner in intellectual endeavors, it will increasingly augment the effectivity and creativity of our human intelligence. Knowledge workers therefore will need to learn how to best prompt the machine with instructions to perform their work. Get started today, experimenting with generative AI tools to develop skills in prompt engineering; a prerequisite skill for creative workers in the decade to come.
With generative AI a major disruptor of our creative work has emerged. Businesses and the world at large will show little patience to apply the new emerging technologies to promote swiftly our level of productivity and content generation. So, be prepared to invest significant time and effort to master the art of creativity in a world dominated by generative AI.
At the same time, we also need to be careful that we seriously consider what these new technologies mean for being a creative human today and how much importance we wish to assign to the role of human authenticity in art and content. In other words, with generative AI at the forefront of our work existence what will our relationship with creativity be? It was Einstein who said that creativity is intelligence having fun. Creative work is thus also something that brings meaning and emotion to the lives of humans.
From that perspective, businesses and society will be responsible to decide how much of the creative work will ultimately be done by AI and how much by humans. Finding the balance here will be an important challenge when we move ahead with integrating generative AI in our daily work existence.
In conclusion, while generative AI has captivated us with its impressive capabilities in the virtual realm, its impact on innovation in the physical world remains limited. The reliance on existing data sets, the overshadowing of human creativity, and the inability to comprehend the complexities of the physical realm contribute to this paradox. It is crucial to acknowledge the inherent limitations of generative AI and strive for a balanced approach that leverages AI as a tool to augment human creativity and ingenuity rather than replacing it. True progress lies in recognizing the collaborative potential between human imagination and machine intelligence, unlocking the true power of AI to drive innovation and push the boundaries of human achievement.
"AI has the potential to amplify human potential, but it is the human touch that will always be essential for genuine innovation."
“Everything that’s not forbidden by the laws of nature is achievable, given the right knowledge” – The Beginning of Infinity
In today's rapidly advancing technological landscape, artificial intelligence (AI) has become a transformative force, revolutionizing various industries. Generative AI, in particular, has garnered significant attention for its ability to create novel content in the virtual realm, such as art, music, and text. However, as generative AI continues to impress with its creative capabilities, concerns have arisen regarding its impact on innovation in the physical realm. This essay delves into the paradox of generative AI, highlighting its limitations in fostering genuine innovation in the physical world.
The AI revolution is here. Generative AI products across audio, video, image, and text are being developed at an astonishing pace. The current AI revolution marks an important milestone in humanity’s unrelenting march in the digital realm. First came the Internet, then the smartphone, and now AI. I am convinced that developing an Artificial General Intelligence that has human-like cognitive abilities to perform a wide range of tasks across different domains is only a matter of time.
We’ve had AI and machine-learning-based tools in our software products for some time, but the current set of AI tools feel radically different. These AI tools such as ChatGPT are so intelligent, contextual, and advanced that simple text-based prompts can output complex code, original poems, and captivating stories. These tools can also comprehend intricate text, skillfully combine different concepts, and produce ingenious ideas. No wonder that ChatGPT has become the fastest and the most widely adopted product in a record time. Many sectors— education, health, research, academia, and government — are ripe for disruption. The possibilities from here on are endless.
When was the last time such a breakthrough innovation happened in the physical realm?
I recently came across a social media post that showed a giant poster wrapped around an unfinished building. The ad sarcastically quips, “Hey ChatGPT, finish this building…” pointing to the inadequacy of ChatGPT to do physical tasks.
It made me wonder as to why the pace and scale of innovation in infrastructure and industry is nowhere near the speed of digital innovation?
Consider cement concrete, one of the foundational pillars of modern civilization. Cement concrete is a mixture of Portland cement, water, sand, and gravel. It is durable, sturdy, can withstand wide temperature and pressure fluctuations. It is also incredibly resistant to wind and water erosion. Because of these advantages, concrete is used everywhere: bridges, dams, houses, hospitals, roads, and all the towering skyscrapers.
However, for all of its advantages, constructing buildings and paving roads with concrete is tedious. Concrete is not good at tensile stress, so it must be reinforced with iron bars, which warrants huge labour. And then, you need to let concrete settle for a considerable time to strengthen. It may be sturdy, but it takes a ridiculous amount of time to build anything with concrete.
So it’s unbelievable to think that despite being invented over 2000 years ago, the basic formula and process for making concrete remain unchanged, with few truly disruptive improvements. The majority of the global infrastructure is built on cement concrete, which is known to have a significant environmental footprint, accounting for around 8% of the world’s carbon dioxide (CO2) emissions. Innovations have been made to reduce this environmental impact, such as “green” or “sustainable” concrete, but these are not yet in widespread use and do not essentially change the nature of concrete itself. The fundamental use and production of concrete remain similar to what it was hundreds, if not thousands of years ago.
Why haven’t we discovered an alternative to cement concrete that’s cheaper, more durable, and far easier to operate? Why isn’t there a disruptive innovation in infrastructure?
Prefabricated building technology was one improvisation that significantly reduced the time to build a structure. But we don’t see it around us because the technology is prohibitively expensive. And the far-fetched idea of printing buildings on site at scale is still a fantasy. In the age of digital acceleration, we are living a tale of physical stagnation.
Even roads too. The Covid pandemic proved that we can only go so far with remote work. People have a fundamental need to travel, and we need to pave thousands and thousands of kilometres of roads to sustain our growing population. How are we going to do it with asphalt and concrete? They are getting expensive by the day and just aren’t good enough for a 21st-century world. Why isn’t there a cheaper and faster way to lay roads and fix our potholes?
Even for our energy needs, we rely heavily on fossil fuels. It will be many decades before we reduce our dependence on coal, oil and natural gas. Why haven’t we solved nuclear fusion that can provide abundant energy for thousands of years to come? Even with nuclear fission, all the world’s nuclear reactors use designs that were created decades ago. More advance technologies such as Dyson spheres and space travel are still confined to sci-fi novels.
We live in a world where smartphones replace libraries, where artificial intelligence redefines work, and where the Internet connects us in an instant. Yet, we also live in a world where cement is still cement, engines still spit out smoke, and electricity is generated as it was generations ago.
We need to innovate and radically improve these processes. To ensure sufficient food for everyone, solve climate change, fix our energy security, and construct roads and buildings rapidly, we need disruptive innovation across these sectors too.
Despite the technological progress in software and smartphones, we have
stagnated in the physical realm. We are able to manipulate binary bits, not physical atoms.
It’s not just about a lack of innovation. Even with existing processes, over time, you expect efficiencies to come in and things to become cheaper. But the costs of building things in the physical world are becoming expensive, hindering rapid progress.
So it’s not enough to discover new materials and new processes; we need to make them cheap for them to be deployed on a mass scale. Like how ChatGPT upended workflows across every conceivable sector, we need innovation that makes paving roads, buildings, and high-speed rails cheaper, faster and lasts a long time.
Undoubtedly, these tasks present significant challenges; otherwise, we would have already accomplished them. But what’s worrying is we have stagnated so much that there is not much progress in these sectors. Our best minds are going for computer science engineering. We need them to work on industrial engineering, too.
Some hard problems in the physical realm that we need to urgently solve:
1. How can we build mega infrastructure projects at a rapid speed and scale?
2. How can we make trees grow 10x as fast without disrupting the ecological balance?
3. How do we harness Nuclear fusion to create abundant energy from atoms?
4. Can we discover a cheaper, faster, more durable alternative to concrete and asphalt?
5. Flying cars and personal aircrafts?
Now these are not forbidden by any laws of physics. So it’s a matter of us developing the knowledge and capability to make it possible.
Of course, there is hope! When the Covid pandemic shook the world, our science & tech community developed vaccines at a breathtaking pace. mRNA vaccines, which used to be confined to esoteric academic journals, became a reality. We built all these vaccines and delivered them at scale. That’s a truly disruptive innovation in the health sector. With the arrival of ChatGPT, we now have the intelligence to help us in the physical realm. Maybe it will help us speed up our physical innovation trajectory.
Here I am adding McKinsey report on state of AI few observation of their organisations.
Three Possible Futures
An explosion of AI-assisted innovation
Today, most businesses recognize the importance of adopting AI to promote the efficiency and performance of its human workforce. For example, AI is being used to augment health care professionals’ job performance in high-stakes work, advising physicians during surgery and using it as a tool in cancer screenings. It’s also being used in customer service, a lower-stakes context. And robotics is used to make warehouses run with greater speed and reliability, as well as reducing costs.
With the arrival of generative AI, we’re seeing experiments with augmentation in more creative work. Not quite two years ago, Github introduced Github Copilot, an AI “pair programmer” that aids the human writing code. More recently, designers, filmmakers, and advertising execs have started using image generators such as DALL-E 2. These tools don’t require users to be very tech savvy. In fact, most of these applications are so easy to use that even children with elementary-level verbal skills can use them to create content right now. Pretty much everyone can make use of them.
This scenario isn’t (necessarily) a threat to people who do creative work. Rather than putting many creators out of work, AI will support humans to do the work they already perform, but simply allowing them to do it with greater speed and efficiency. In this scenario, productivity would rise, as reliance on generative AI tools that use natural language reduces the time and effort required to come up with new ideas or pieces of text. Of course, humans will still have to devote time to possibly correct and edit the newly generated information, but, overall, creative projects should be able to move forward more quickly.
We can already glimpse what such future holds: With reduced barriers to entry, we can expect many more people to engage in creative work. Github’s Copilot doesn’t replace the human writing code, but it does make coding easier for novices, as they can rely on the knowledge embedded within the model and vast reams of data rather than having to learn everything from scratch themselves. If more people learn “prompt engineering” — the skill of asking the machine the right questions — AI will be able to produce very relevant and meaningful content that humans will only need to edit somewhat before they can put it to use. This higher level of efficiency can be facilitated by having people speak instructions to a computer, via advanced voice-to-text algorithms, which will then be interpreted and executed by an AI like ChatGPT.
The ability to quickly retrieve, contextualize, and easily interpret knowledge may be the most powerful business application of large-language models. A natural language interface combined with a powerful AI algorithm will help humans in coming up more quickly with a larger number of ideas and solutions that they subsequently can experiment with to eventually reveal more and better creative output. Overall, this scenario paints a world of faster innovation where machine augmented human creativity will enable mainly rapid iteration.
Machines monopolize creativity
A second possible scenario is that unfair algorithmic competition and inadequate governance leads to the crowding out of authentic human creativity. Here, human writers, producers, and creators are drowned out by a tsunami of algorithmically generated content, with some talented creators even opting out of the market. If that would happen, then an important question that we need to address is: How will we generate new ideas?
A nascent version of this scenario might already be happening. For example, recent lawsuits against prominent generative AI platforms allege copyright infringement on a massive scale. What makes this issue even more fraught is that intellectual-property laws have not caught up with the technological progress made in the field of AI research. It’s quite possible that governments will spend decades fighting over how to balance incentives for technical innovation while retaining incentives for authentic human creation — a route that would be a terrific loss for human creativity.
In this scenario, generative AI significantly changes the incentive structure for creators, and raises risks for businesses and society. If cheaply made generative AI undercuts authentic human content, there’s a real risk that innovation will slow down over time as humans make less and less new art and content. Creators are already in intense competition for human attention spans, and this kind of competition — and pressure — will only rise further if there is unlimited content on demand. Extreme content abundance, far beyond what we’ve seen with any digital disruption to date, will inundate us with noise, and we’ll need to find new techniques and strategies to manage the deluge.
This scenario could also mean fundamental changes to what content creation looks like. If production costs fall close to nothing, that opens up the possibility of reaching specific — and often less included — audiences through extreme personalization and versioning. In fact, we expect the pressure to personalize to go up fast as generative AI carries such great potential to satisfy the need to create content that is increasingly representative of the specific consumer. As a case in point, Buzzfeed recently announced it will personalize their content such as quizzes and tailor-made rom-com pitches with OpenAI’s tools. (They don’t plan to use generative AI in their newsroom, however.)
If the practice of enhanced personalized experiences is applied broadly, then we run the risk to lose the shared experience of watching the same film, reading the same book, and consuming the same news. In that case, it will be easier to create politically divisive viral content, and significant volumes of mis/disinformation, as the average quality of content declines alongside the share of authentic human content. Both would likely worsen filter bubble effects.
Yet even in this relative dystopia, there remains a significant role for humans to make recommendations of existing content in this ecosystem. As in other very large content markets, like music streaming services, curation will become more valuable relative to creation as search costs rise. At the same time, however, high search costs will lock-in existing artists at the expense of new ones, concentrate and bifurcate the market. This will then result in a small handful of established artists dominating the market with a long tail of creators retaining minimal market share.
“Human-made” commands a premium.
The third potential scenario that we could see develop is one where the “techlash” resumes with a focus against algorithmically generated content. One plausible effect of being inundated with synthetic creative outputs is that people will begin to value authentic creativity more again and may be willing to pay a premium for it. While generative models demonstrate remarkable and sometimes emergent capabilities, they suffer from problems with accuracy, frequently producing text that sounds legitimate but is riddled with factual errors and erroneous logic. For obvious reasons, humans might demand greater accuracy from their content providers, and therefore may start to rely more on trusted human sources rather than machine-generated information.
In this scenario, humans maintain a competitive advantage against algorithmic competition. The uniqueness of human creativity including awareness of social and cultural context, both across borders and through time will become important leverage. Culture changes much more quickly than generative algorithms can be trained, so humans maintain a dynamism that algorithms cannot compete against. In fact, it is likely that humans should retain the ability to make significant leaps of creativity, even if algorithmic capabilities improve incrementally.
In the development of this scenario, it follows that political leadership taking action to strengthen governance of information spaces will be needed to deal with the downside risks that could emerge. For instance, content moderation needs are likely to explode as information platforms are overwhelmed with false or misleading content, and therefore require human intervention and carefully designed governance frameworks to counter.
How to Prepare for Generative AI
Creativity has always been a critical pre-requisite to any company’s innovation process and hence competitiveness. Not too long ago, the business of creativity was a uniquely human endeavor. However, as we illustrate, with the arrival of generative AI, this is all about to change. So, to be prepared, we need to understand the accompanying threats and challenges. Once we understand what is to change and how, we can prepare for a future where the creativity business will be a function of human–machine collaborations. Below, we provide three recommendations that workers should consider as they adopt generative AI to create business value and profit in today’s creative industries.
Prepare for disruption, and not only to your job. Generative AI could be the biggest change in the cost structure of information production since the creation of the printing press in 1439. The centuries that followed featured rapid innovation, socio-political volatility, and economic disruption across a swathe of industries as the cost of acquiring knowledge and information fell precipitously. We are in the very early stages of the generative AI revolution. We expect the near future therefore to be more volatile than the recent past.
Invest in your ontology. Codifying, digitizing, and structuring the knowledge you create will be a critical value driver in the decades to come. Generative AI and large language models enable knowledge and skills to transmit more easily across teams and business units, accelerating learning and innovation.
Get comfortable talking to AI. As AI becomes a partner in intellectual endeavors, it will increasingly augment the effectivity and creativity of our human intelligence. Knowledge workers therefore will need to learn how to best prompt the machine with instructions to perform their work. Get started today, experimenting with generative AI tools to develop skills in prompt engineering; a prerequisite skill for creative workers in the decade to come.
With generative AI a major disruptor of our creative work has emerged. Businesses and the world at large will show little patience to apply the new emerging technologies to promote swiftly our level of productivity and content generation. So, be prepared to invest significant time and effort to master the art of creativity in a world dominated by generative AI.
At the same time, we also need to be careful that we seriously consider what these new technologies mean for being a creative human today and how much importance we wish to assign to the role of human authenticity in art and content. In other words, with generative AI at the forefront of our work existence what will our relationship with creativity be? It was Einstein who said that creativity is intelligence having fun. Creative work is thus also something that brings meaning and emotion to the lives of humans.
From that perspective, businesses and society will be responsible to decide how much of the creative work will ultimately be done by AI and how much by humans. Finding the balance here will be an important challenge when we move ahead with integrating generative AI in our daily work existence.
In conclusion, while generative AI has captivated us with its impressive capabilities in the virtual realm, its impact on innovation in the physical world remains limited. The reliance on existing data sets, the overshadowing of human creativity, and the inability to comprehend the complexities of the physical realm contribute to this paradox. It is crucial to acknowledge the inherent limitations of generative AI and strive for a balanced approach that leverages AI as a tool to augment human creativity and ingenuity rather than replacing it. True progress lies in recognizing the collaborative potential between human imagination and machine intelligence, unlocking the true power of AI to drive innovation and push the boundaries of human achievement.
"AI has the potential to amplify human potential, but it is the human touch that will always be essential for genuine innovation."
“Everything that’s not forbidden by the laws of nature is achievable, given the right knowledge” – The Beginning of Infinity
Reference:-
1) khan academy
2) AI laws an implication published by iiitH
3) McKinsey state of AI report
4)NITI ayoga vision document on AI
5) personal experience with AI tools such as chatgpt,chatgpt3,ImgAi,ineuronai.
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