Five predictions about artificial intelligence in 2030
Here are five bold predictions about what the world of artificial intelligence will look like in 2030.
What changes will the field of artificial intelligence undergo by 2030? Rob Toews, a venture capitalist at Radical Ventures, has released five AI predictions for the year 2030.
Here are five bold predictions about what the world of artificial intelligence will look like in 2030. Whether you agree or disagree with these predictions, it is hoped that they will provoke your thoughts.
NVIDIA's market value will be significantly lower than it is now, and Intel's price will be significantly higher than it is now.
NVIDIA is currently one of the hottest companies in the world. It is the biggest beneficiary of the current generative AI boom, with its market value soaring from less than $300 billion at the end of 2022 to over $2 trillion today.
However, NVIDIA's position as the single dominant supplier of AI chips cannot and will not last.
The products that NVIDIA has built are hard to replicate, but not impossible. The resurgent AMD is becoming a reliable alternative supplier of advanced GPUs, with its cutting-edge new MI300 chips about to be widely used. Large tech companies—Amazon, Microsoft, Alphabet, Meta—are heavily investing in the development of their own AI chips to reduce their dependence on NVIDIA. Sam Altman of OpenAI is seeking funding of up to tens of trillions of dollars to establish a new chip company to diversify the global supply of AI hardware.
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As demand for AI chips continues to grow in the coming years, relentless market forces will ensure that more competitors enter, supply increases, prices drop, profit margins tighten, and NVIDIA's market share will decline.Additionally, as the market matures in the coming years, the primary type of artificial intelligence computing workload will shift from training to inference: that is, from building AI models to deploying these models in real-world environments. Nvidia's highly specialized chips are unparalleled in training models. However, inference can be accomplished with cheaper, more commoditized chips, which could diminish Nvidia's market advantage and create opportunities for competitors.
This is not to say that Nvidia will not remain an important part of the AI ecosystem by 2030. But its current stock price surge (as of the writing of this article, this has made it the third most valuable company in the world) compared to Amazon or Alphabet—seen in hindsight, this appears to be an irrational exuberance.
In the meantime: What sets Intel apart from almost every other chip company in the world?
It manufactures its own chips.
Nvidia, AMD, Qualcomm, Broadcom, Alphabet, Microsoft, Amazon, Tesla, Cerebras, SambaNova, Groq: these companies do not manufacture their own chips. Instead, they design chips and then rely on other companies—most importantly, TSMC—to produce these chips for them.
Intel alone owns and operates its own chip manufacturing facilities.
The ability to manufacture chips has become a significant geopolitical asset. For example: China's complete dependence on foreign semiconductor suppliers has allowed the United States to hinder China's domestic AI industry by banning the import of AI chips to China.
U.S. policymakers are keenly aware of the vulnerability posed by the extreme concentration of chip manufacturing in Taiwan today. Promoting advanced semiconductor manufacturing within the United States has become a top policy priority for the U.S. government. U.S. legislators are taking decisive action to advance this goal, including committing up to $280 billion in funding under the 2022 CHIPS Act.
It's no secret that over the past decade, Intel has fallen behind TSMC in the ability to manufacture cutting-edge chips. However, it remains one of the few companies in the world capable of manufacturing advanced semiconductors. Under the leadership of CEO Pat Gelsinger, who took office in 2021, Intel has reprioritized chip manufacturing and adopted an ambitious strategy to reclaim its former status as a global leader in chip manufacturing. Recent signs indicate that the company is making progress towards this goal.
Perhaps most importantly: as the leader in domestic U.S. chip manufacturing, there is no other alternative.In a recent speech, Gina Raimondo, the U.S. Secretary of Commerce leading the Biden administration's efforts in artificial intelligence and chip work, directly acknowledged this: "Intel is America's champion chip company." In short, the United States needs Intel. This is a good omen for Intel's business prospects.
Nvidia currently has a market value of $2.2 trillion. Intel's valuation is $186 billion, which is an order of magnitude smaller. We expect that by 2030, this gap will narrow significantly.
We will interact with various forms of artificial intelligence in our daily lives as naturally as we interact with other humans today.
Despite the current global hype around artificial intelligence, the number of actual touchpoints between the average person and cutting-edge AI systems today is limited: perhaps occasionally querying ChatGPT or Google Bard/Gemini.
By 2030, this situation will change dramatically.
We will use artificial intelligence as our personal assistants, tutors, career advisors, therapists, accountants, and lawyers.
They will be ubiquitous in our work lives: conducting analyses, writing code, building products, selling products, supporting customers, coordinating across teams and organizations, and making strategic decisions.
Yes, by 2030, humans will universally have artificial intelligence as significant others.
As with any new technology, there will be an adoption curve. Some segments of the population will more readily adapt to interactions with new AI companions; others will resist for a longer period.Artificial intelligence will be able to do many of the things humans do today, only more cheaply, more quickly, and more reliably.
Over a hundred thousand humanoid robots will be deployed in the real world.
Today's AI boom is almost entirely unfolding in the digital realm.
Generative models that can engage in knowledgeable conversations on any topic, generate high-quality videos on demand, or write complex code represent significant advancements in artificial intelligence. But these advancements are all happening in the world of software, the world of bits. There is an entire domain waiting for transformation by today's cutting-edge AI: the physical world, the world of atoms.
Of course, the field of robotics has existed for decades. Today, there are millions of robots operating around the world, capable of automatically performing different types of physical activities.
But the capabilities of today's robots are limited, and their intelligence is limited. They are typically designed for specific tasks, such as moving boxes around a warehouse, completing specific steps in a manufacturing process, or vacuuming floors. They are far from possessing the fluid adaptability and general understanding of large language models like ChatGPT.
This situation will change in the coming years. Generative artificial intelligence will conquer the world of atoms—and in comparison, it will make everything that has happened in the field of AI so far seem insignificant.
Tracing back to the dawn of digital computing, a recurring theme in the technology field has been to make hardware platforms as general-purpose as possible, while reserving as much flexibility as possible for the software layer.
This principle was championed by none other than the father of computer and AI knowledge, Alan Turing himself, who immortalized this principle in the concept of the "Turing machine": a machine capable of executing any possible algorithm.The early development of digital computers confirmed Turing's fundamental insights. In the 1940s, people built different physical computers for different tasks: one for calculating the trajectories of missiles, another for decrypting enemy information. But by the 1950s, general-purpose, fully programmable computers had become the dominant computing architecture. Their versatility and adaptability across use cases proved to be a decisive advantage: with the writing of new software, they could be continually updated and used for any new application.
Consider, in more recent history, how many different physical devices have been consolidated into a single product, the iPhone, thanks to the genius of Steve Jobs and others: telephone, camera, video recorder, audio recorder, MP3 player, GPS navigator, e-book reader, gaming device, flashlight, compass.
In the coming years, we will see the same transformation in the field of robotics: moving away from specialized machines defined by narrow use cases towards more general, flexible, and adaptable general-purpose hardware platforms.
What will this general-purpose hardware platform look like? What form factor does it need to operate flexibly in a variety of different physical environments?
The answer is clear: it needs to look like a human.
Our entire civilization is designed and built by humans and for humans. Our physical infrastructure, our tools, our products, the size of our buildings, the size of our rooms, the size of our doors: all of these are optimized for the human body. If we want to develop a general-purpose robot that can operate in factories, warehouses, hospitals, shops, schools, hotels, and our homes, then this robot needs to be shaped like us. No other factor can achieve the same effect.
This is why the opportunity for humanoid robots is so enormous. Bringing cutting-edge artificial intelligence into the real world is the next great frontier of AI.
Large language models will automate a vast amount of cognitive work in the coming years. At the same time, humanoid robots will automate a vast amount of physical labor.
These robots are no longer a distant science fiction dream. Although most people are not yet aware, humanoid robots are about to be deployed into the real world.
Tesla is investing heavily in the development of a humanoid robot called Optimus. The company's goal is to begin shipping robots to customers by 2025.Tesla CEO Elon Musk made no bones about how important he expects this technology to be for the company and the world: "I'm surprised people don't realize the importance of the Optimus robot program. The significance of Optimus will become apparent in the coming years. Those who are insightful or who observe, listen, and think carefully will understand that Optimus will eventually be more valuable than Tesla's car business, more valuable than full self-driving."
Some young startups have also made rapid progress here.
Just last week, Bay Area-based Figure announced the completion of a $675 million funding round, with investors including Nvidia, Microsoft, OpenAI, and Jeff Bezos. A few months ago, the company released an impressive video of a humanoid robot making coffee.
Another leading humanoid startup, 1X Technologies, announced a $100 million funding round in January. 1X has already offered sales of a humanoid robot and plans to release the next generation soon.
In the coming years, these companies will gradually shift from small-scale customer pilots to large-scale production. By the end of this century, it is expected that hundreds of thousands (if not millions) of humanoid robots will be deployed in the real world.
"Agents" and "AGI" will become outdated terms no longer widely used
The two hottest topics in artificial intelligence today are agents and artificial general intelligence (AGI).
Agents are artificial intelligence systems that can perform loosely defined tasks: for example, planning and booking your upcoming trip. AGI refers to artificial intelligence systems that achieve or exceed human capabilities in all aspects.
When people look forward to the state of artificial intelligence in 2030, agents and/or AGI are usually the most important.
However, we predict that by 2030, these two terms will not even be widely used. Why? Because they will no longer be relevant as independent concepts.Let's start with the term "Agent."
By 2030, agency will become a fundamental element of any advanced AI system.
The overarching term "agent" that we use today is actually just a set of core capabilities that any truly intelligent entity possesses: the ability to think long-term, plan, and take actions to pursue open-ended goals. Becoming "agentive" is the natural and inevitable end state of today's AI. Cutting-edge AI systems in 2030 will not only generate outputs when prompted but will also generate outputs on their own. They will get work done.
In other words, "agent" will no longer be an interesting subfield within AI research as it is today. AI will become agents, and agents will become AI. Thus, the term "agent" as a separate concept will be useless.
What about the term "AGI"?
It is a fundamental fact that AI is inherently different from human intelligence, something that people often fail to grasp.
In the coming years, AI will become more powerful, incredibly so. But we will stop conceptualizing its trajectory as moving towards some kind of "general" end state, especially one whose contours are defined by human capabilities.
AI great Yann LeCun summarized it well: "There is no such thing as AGI... even humans are specialized."
Using human intelligence as the ultimate anchor and benchmark for the development of AI fails to recognize all the powerful, profound, unexpected, socially beneficial, and entirely non-human capabilities that machine intelligence might possess.
By 2030, AI will be more powerful than humans, thus changing our world. It will also continue to lag behind in other aspects of human capabilities. If AI can understand and explain every detail of human biology down to the atomic level, who cares whether it has the "generality" to fully match human capabilities?The concept of artificial general intelligence is not particularly coherent. As artificial intelligence rapidly evolves in the coming years, the term will become increasingly unhelpful and irrelevant.
Artificial intelligence-driven unemployment will become one of the most widely discussed political and social issues.
Concerns about technology-induced unemployment are a common theme in modern society, dating back to the Industrial Revolution and the Luddites. The AI era is no exception.
However, discussions about the impact of artificial intelligence on the job market have thus far been largely theoretical and long-term, confined to academic research and think tank white papers.
The change in this situation will be much more abrupt than most people imagine. Before the end of this decade, unemployment due to artificial intelligence will become a concrete and urgent reality in the lives of ordinary citizens.
Last month, fintech giant Klarna announced that its new customer service AI system is handling the work of 700 full-time human agents. Turnitin recently projected that the company will lay off 20% of its workforce within the next 18 months due to advancements in artificial intelligence.
In the coming years, organizations will find that they can increase profitability and productivity by using artificial intelligence to perform an increasing number of tasks previously done by humans. This will occur across industries and salary levels: from customer service representatives to accountants, from data scientists to cashiers, from lawyers to security guards, from court reporters to pathologists, from taxi drivers to management consultants, from journalists to musicians.
This is not a distant possibility. Today, the technology is already good enough in many cases.
If we are honest with ourselves, one of the main reasons we are so excited about artificial intelligence (and one of the main reasons it offers such transformative economic opportunities) is that it will be able to do things more cheaply, quickly, and in greater quantities than humans do today. More accurately. Once artificial intelligence can deliver on this promise, the need and economic rationale for employing as many people as we do today in most fields will diminish. Almost by definition, in order for artificial intelligence to have an impact on society and the economy, it will take jobs away from people. Of course, new jobs will also be created, but not as quickly, and not in as great numbers, at least not initially.Unemployment will bring about significant short-term pain and chaos. Political movements and leaders will strongly oppose this trend. Other parts of society will also vigorously support the benefits of technology and artificial intelligence. Civil unrest and protests will be inevitable; there is no doubt that they will sometimes turn violent.
Citizens will call on their elected officials to take action in a certain direction. Creative policy proposals like universal basic income will shift from fringe theories to enacted legislation.
There will be no simple solutions or clear moral choices. Political stances and social identities will increasingly depend on an individual's view of how society should guide the spread of artificial intelligence throughout the economy.
*Disclaimer: This article is the original creation of the author. The content of the article represents their personal views. Our reposting is solely for sharing and discussion, and does not represent our endorsement or agreement. If you have any objections, please contact the backend.
Here are five bold predictions about what the world of artificial intelligence will look like in 2030.
What changes will the field of artificial intelligence undergo by 2030? Rob Toews, a venture capitalist at Radical Ventures, has released five AI predictions for the year 2030.
Here are five bold predictions about what the world of artificial intelligence will look like in 2030. Whether you agree or disagree with these predictions, it is hoped that they will provoke your thoughts.
NVIDIA's market value will be significantly lower than it is now, and Intel's price will be significantly higher than it is now.
NVIDIA is currently one of the hottest companies in the world. It is the biggest beneficiary of the current generative AI boom, with its market value soaring from less than $300 billion at the end of 2022 to over $2 trillion today.
However, NVIDIA's position as the single dominant supplier of AI chips cannot and will not last.
The products that NVIDIA has built are hard to replicate, but not impossible. The resurgent AMD is becoming a reliable alternative supplier of advanced GPUs, with its cutting-edge new MI300 chips about to be widely used. Large tech companies—Amazon, Microsoft, Alphabet, Meta—are heavily investing in the development of their own AI chips to reduce their dependence on NVIDIA. Sam Altman of OpenAI is seeking funding of up to tens of trillions of dollars to establish a new chip company to diversify the global supply of AI hardware.
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As demand for AI chips continues to grow in the coming years, relentless market forces will ensure that more competitors enter, supply increases, prices drop, profit margins tighten, and NVIDIA's market share will decline.Additionally, as the market matures in the coming years, the primary type of artificial intelligence computing workload will shift from training to inference: that is, from building AI models to deploying these models in real-world environments. Nvidia's highly specialized chips are unparalleled in training models. However, inference can be accomplished with cheaper, more commoditized chips, which could diminish Nvidia's market advantage and create opportunities for competitors.
This is not to say that Nvidia will not remain an important part of the AI ecosystem by 2030. But its current stock price surge (as of the writing of this article, this has made it the third most valuable company in the world) compared to Amazon or Alphabet—seen in hindsight, this appears to be an irrational exuberance.
In the meantime: What sets Intel apart from almost every other chip company in the world?
It manufactures its own chips.
Nvidia, AMD, Qualcomm, Broadcom, Alphabet, Microsoft, Amazon, Tesla, Cerebras, SambaNova, Groq: these companies do not manufacture their own chips. Instead, they design chips and then rely on other companies—most importantly, TSMC—to produce these chips for them.
Intel alone owns and operates its own chip manufacturing facilities.
The ability to manufacture chips has become a significant geopolitical asset. For example: China's complete dependence on foreign semiconductor suppliers has allowed the United States to hinder China's domestic AI industry by banning the import of AI chips to China.
U.S. policymakers are keenly aware of the vulnerability posed by the extreme concentration of chip manufacturing in Taiwan today. Promoting advanced semiconductor manufacturing within the United States has become a top policy priority for the U.S. government. U.S. legislators are taking decisive action to advance this goal, including committing up to $280 billion in funding under the 2022 CHIPS Act.
It's no secret that over the past decade, Intel has fallen behind TSMC in the ability to manufacture cutting-edge chips. However, it remains one of the few companies in the world capable of manufacturing advanced semiconductors. Under the leadership of CEO Pat Gelsinger, who took office in 2021, Intel has reprioritized chip manufacturing and adopted an ambitious strategy to reclaim its former status as a global leader in chip manufacturing. Recent signs indicate that the company is making progress towards this goal.
Perhaps most importantly: as the leader in domestic U.S. chip manufacturing, there is no other alternative.In a recent speech, Gina Raimondo, the U.S. Secretary of Commerce leading the Biden administration's efforts in artificial intelligence and chip work, directly acknowledged this: "Intel is America's champion chip company." In short, the United States needs Intel. This is a good omen for Intel's business prospects.
Nvidia currently has a market value of $2.2 trillion. Intel's valuation is $186 billion, which is an order of magnitude smaller. We expect that by 2030, this gap will narrow significantly.
We will interact with various forms of artificial intelligence in our daily lives as naturally as we interact with other humans today.
Despite the current global hype around artificial intelligence, the number of actual touchpoints between the average person and cutting-edge AI systems today is limited: perhaps occasionally querying ChatGPT or Google Bard/Gemini.
By 2030, this situation will change dramatically.
We will use artificial intelligence as our personal assistants, tutors, career advisors, therapists, accountants, and lawyers.
They will be ubiquitous in our work lives: conducting analyses, writing code, building products, selling products, supporting customers, coordinating across teams and organizations, and making strategic decisions.
Yes, by 2030, humans will universally have artificial intelligence as significant others.
As with any new technology, there will be an adoption curve. Some segments of the population will more readily adapt to interactions with new AI companions; others will resist for a longer period.Artificial intelligence will be able to do many of the things humans do today, only more cheaply, more quickly, and more reliably.
Over a hundred thousand humanoid robots will be deployed in the real world.
Today's AI boom is almost entirely unfolding in the digital realm.
Generative models that can engage in knowledgeable conversations on any topic, generate high-quality videos on demand, or write complex code represent significant advancements in artificial intelligence. But these advancements are all happening in the world of software, the world of bits. There is an entire domain waiting for transformation by today's cutting-edge AI: the physical world, the world of atoms.
Of course, the field of robotics has existed for decades. Today, there are millions of robots operating around the world, capable of automatically performing different types of physical activities.
But the capabilities of today's robots are limited, and their intelligence is limited. They are typically designed for specific tasks, such as moving boxes around a warehouse, completing specific steps in a manufacturing process, or vacuuming floors. They are far from possessing the fluid adaptability and general understanding of large language models like ChatGPT.
This situation will change in the coming years. Generative artificial intelligence will conquer the world of atoms—and in comparison, it will make everything that has happened in the field of AI so far seem insignificant.
Tracing back to the dawn of digital computing, a recurring theme in the technology field has been to make hardware platforms as general-purpose as possible, while reserving as much flexibility as possible for the software layer.
This principle was championed by none other than the father of computer and AI knowledge, Alan Turing himself, who immortalized this principle in the concept of the "Turing machine": a machine capable of executing any possible algorithm.The early development of digital computers confirmed Turing's fundamental insights. In the 1940s, people built different physical computers for different tasks: one for calculating the trajectories of missiles, another for decrypting enemy information. But by the 1950s, general-purpose, fully programmable computers had become the dominant computing architecture. Their versatility and adaptability across use cases proved to be a decisive advantage: with the writing of new software, they could be continually updated and used for any new application.
Consider, in more recent history, how many different physical devices have been consolidated into a single product, the iPhone, thanks to the genius of Steve Jobs and others: telephone, camera, video recorder, audio recorder, MP3 player, GPS navigator, e-book reader, gaming device, flashlight, compass.
In the coming years, we will see the same transformation in the field of robotics: moving away from specialized machines defined by narrow use cases towards more general, flexible, and adaptable general-purpose hardware platforms.
What will this general-purpose hardware platform look like? What form factor does it need to operate flexibly in a variety of different physical environments?
The answer is clear: it needs to look like a human.
Our entire civilization is designed and built by humans and for humans. Our physical infrastructure, our tools, our products, the size of our buildings, the size of our rooms, the size of our doors: all of these are optimized for the human body. If we want to develop a general-purpose robot that can operate in factories, warehouses, hospitals, shops, schools, hotels, and our homes, then this robot needs to be shaped like us. No other factor can achieve the same effect.
This is why the opportunity for humanoid robots is so enormous. Bringing cutting-edge artificial intelligence into the real world is the next great frontier of AI.
Large language models will automate a vast amount of cognitive work in the coming years. At the same time, humanoid robots will automate a vast amount of physical labor.
These robots are no longer a distant science fiction dream. Although most people are not yet aware, humanoid robots are about to be deployed into the real world.
Tesla is investing heavily in the development of a humanoid robot called Optimus. The company's goal is to begin shipping robots to customers by 2025.Tesla CEO Elon Musk made no bones about how important he expects this technology to be for the company and the world: "I'm surprised people don't realize the importance of the Optimus robot program. The significance of Optimus will become apparent in the coming years. Those who are insightful or who observe, listen, and think carefully will understand that Optimus will eventually be more valuable than Tesla's car business, more valuable than full self-driving."
Some young startups have also made rapid progress here.
Just last week, Bay Area-based Figure announced the completion of a $675 million funding round, with investors including Nvidia, Microsoft, OpenAI, and Jeff Bezos. A few months ago, the company released an impressive video of a humanoid robot making coffee.
Another leading humanoid startup, 1X Technologies, announced a $100 million funding round in January. 1X has already offered sales of a humanoid robot and plans to release the next generation soon.
In the coming years, these companies will gradually shift from small-scale customer pilots to large-scale production. By the end of this century, it is expected that hundreds of thousands (if not millions) of humanoid robots will be deployed in the real world.
"Agents" and "AGI" will become outdated terms no longer widely used
The two hottest topics in artificial intelligence today are agents and artificial general intelligence (AGI).
Agents are artificial intelligence systems that can perform loosely defined tasks: for example, planning and booking your upcoming trip. AGI refers to artificial intelligence systems that achieve or exceed human capabilities in all aspects.
When people look forward to the state of artificial intelligence in 2030, agents and/or AGI are usually the most important.
However, we predict that by 2030, these two terms will not even be widely used. Why? Because they will no longer be relevant as independent concepts.Let's start with the term "Agent."
By 2030, agency will become a fundamental element of any advanced AI system.
The overarching term "agent" that we use today is actually just a set of core capabilities that any truly intelligent entity possesses: the ability to think long-term, plan, and take actions to pursue open-ended goals. Becoming "agentive" is the natural and inevitable end state of today's AI. Cutting-edge AI systems in 2030 will not only generate outputs when prompted but will also generate outputs on their own. They will get work done.
In other words, "agent" will no longer be an interesting subfield within AI research as it is today. AI will become agents, and agents will become AI. Thus, the term "agent" as a separate concept will be useless.
What about the term "AGI"?
It is a fundamental fact that AI is inherently different from human intelligence, something that people often fail to grasp.
In the coming years, AI will become more powerful, incredibly so. But we will stop conceptualizing its trajectory as moving towards some kind of "general" end state, especially one whose contours are defined by human capabilities.
AI great Yann LeCun summarized it well: "There is no such thing as AGI... even humans are specialized."
Using human intelligence as the ultimate anchor and benchmark for the development of AI fails to recognize all the powerful, profound, unexpected, socially beneficial, and entirely non-human capabilities that machine intelligence might possess.
By 2030, AI will be more powerful than humans, thus changing our world. It will also continue to lag behind in other aspects of human capabilities. If AI can understand and explain every detail of human biology down to the atomic level, who cares whether it has the "generality" to fully match human capabilities?The concept of artificial general intelligence is not particularly coherent. As artificial intelligence rapidly evolves in the coming years, the term will become increasingly unhelpful and irrelevant.
Artificial intelligence-driven unemployment will become one of the most widely discussed political and social issues.
Concerns about technology-induced unemployment are a common theme in modern society, dating back to the Industrial Revolution and the Luddites. The AI era is no exception.
However, discussions about the impact of artificial intelligence on the job market have thus far been largely theoretical and long-term, confined to academic research and think tank white papers.
The change in this situation will be much more abrupt than most people imagine. Before the end of this decade, unemployment due to artificial intelligence will become a concrete and urgent reality in the lives of ordinary citizens.
Last month, fintech giant Klarna announced that its new customer service AI system is handling the work of 700 full-time human agents. Turnitin recently projected that the company will lay off 20% of its workforce within the next 18 months due to advancements in artificial intelligence.
In the coming years, organizations will find that they can increase profitability and productivity by using artificial intelligence to perform an increasing number of tasks previously done by humans. This will occur across industries and salary levels: from customer service representatives to accountants, from data scientists to cashiers, from lawyers to security guards, from court reporters to pathologists, from taxi drivers to management consultants, from journalists to musicians.
This is not a distant possibility. Today, the technology is already good enough in many cases.
If we are honest with ourselves, one of the main reasons we are so excited about artificial intelligence (and one of the main reasons it offers such transformative economic opportunities) is that it will be able to do things more cheaply, quickly, and in greater quantities than humans do today. More accurately. Once artificial intelligence can deliver on this promise, the need and economic rationale for employing as many people as we do today in most fields will diminish. Almost by definition, in order for artificial intelligence to have an impact on society and the economy, it will take jobs away from people. Of course, new jobs will also be created, but not as quickly, and not in as great numbers, at least not initially.Unemployment will bring about significant short-term pain and chaos. Political movements and leaders will strongly oppose this trend. Other parts of society will also vigorously support the benefits of technology and artificial intelligence. Civil unrest and protests will be inevitable; there is no doubt that they will sometimes turn violent.
Citizens will call on their elected officials to take action in a certain direction. Creative policy proposals like universal basic income will shift from fringe theories to enacted legislation.
There will be no simple solutions or clear moral choices. Political stances and social identities will increasingly depend on an individual's view of how society should guide the spread of artificial intelligence throughout the economy.
*Disclaimer: This article is the original creation of the author. The content of the article represents their personal views. Our reposting is solely for sharing and discussion, and does not represent our endorsement or agreement. If you have any objections, please contact the backend.