The Future of AI: How AI Is Changing the World
Innovations in the field of artificial intelligence continue to shape the future of humanity across nearly every industry. AI is already the main driver of emerging technologies like big data, robotics and IoT, and generative AI has further expanded the possibilities and popularity of AI.
As of 2024, about 42 percent of enterprise-scale companies have actively deployed AI in their business. Plus, 92 percent of companies plan to increase their investments in AI technology from 2025 to 2028.
With so many changes coming at such a rapid pace, here’s what shifts in AI could mean for various industries and society at large.
The Evolution of AI
AI has come a long way since 1952, when the first documented success of an AI computer program was written by Christopher Strachey, whose checkers program completed a whole game on the Ferranti Mark I computer at the University of Manchester. Thanks to developments in machine learning and deep learning, IBM’s Deep Blue defeated chess grandmaster Garry Kasparov in 1997, and the company’s IBM Watson won Jeopardy! in 2011.
Since then, generative AI has spearheaded the latest chapter in AI’s evolution, with OpenAI releasing its first GPT model in 2018. This has culminated in OpenAI developing ChatGPT, leading to a proliferation of tools that can process queries to produce relevant text, audio, images and other types of content.
Other companies have followed suit with competing products of their own, including Google’s Gemini, Anthropic’s Claude and DeepSeek’s R1 and V3 models, which made headlines in early 2025 for approaching parity with competing models at a fraction of the operational cost.
AI has also been used to help sequence RNA for vaccines and model human speech, technologies that rely on model- and algorithm-based machine learning and increasingly focus on perception, reasoning and generalization.
How AI Will Impact the Future
Improved Business Automation
AI, especially generative AI, has already increased task automation for many businesses, and will likely continue to do so in the future. With the rise of chatbots and digital assistants, companies can rely on AI to handle simple conversations with customers and answer basic queries from employees.
AI’s ability to analyze massive amounts of data and convert its findings into convenient visual formats can also accelerate the decision-making process. Company leaders don’t have to spend time parsing through the data themselves, instead using instant insights to make informed decisions.
“If [developers] understand what the technology is capable of and they understand the domain very well, they start to make connections and say, ‘Maybe this is an AI problem, maybe that’s an AI problem,’” said Mike Mendelson, a learner experience designer for NVIDIA. “That’s more often the case than, ‘I have a specific problem I want to solve.’”
Job Disruption
Business automation has naturally led to fears over job losses. Although AI has made gains in the workplace, it’s had an unequal impact on different industries and professions. For example, repetitive jobs like data entry or processing and customer service are already being automated, but the demand for other jobs like machine learning specialists and information security analysts has risen.
Workers in creative positions are more likely to have their jobs augmented by AI, rather than outright replaced. Whether forcing employees to learn new tools or taking over their roles, AI is set to spur upskilling efforts at both the individual and company level.
“One of the absolute prerequisites for AI to be successful in many [areas] is that we invest tremendously in education to retrain people for new jobs,” said Klara Nahrstedt, a computer science professor at the University of Illinois at Urbana-Champaign and director of the school’s Coordinated Science Laboratory.
Data Privacy Issues
Companies require large volumes of data to train the models that power generative AI tools, and this process has come under intense scrutiny. Concerns over companies collecting consumers’ personal data had led the FTC to open an investigation in 2023 into whether OpenAI has negatively impacted consumers through its data collection methods after the company potentially violated European data protection laws.
In response, the Biden-Harris administration developed an AI Bill of Rights in October 2023 that listed data privacy as one of its core principles. Although this legislation doesn’t carry much legal weight, it reflects the growing push to prioritize data privacy and compel AI companies to be more transparent and cautious about how they compile training data.
Increased Regulation
AI could shift the perspective on certain legal questions, depending on how generative AI lawsuits continue to unfold. For example, the issue of intellectual property has come to the forefront in light of copyright lawsuits filed against OpenAI and Anthropic by writers, musicians and companies like The New York Times. These lawsuits affect how the U.S. legal system interprets what is private and public property, and a loss could spell major setbacks for OpenAI and its competitors.
Ethical issues that have surfaced in connection to generative AI have placed more pressure on the U.S. government to take a stronger stance. Despite this, the Trump administration’s AI Action Plan unveiled in 2025 emphasizes a largely hands-off approach to AI regulation.
Climate Change Concerns
On a far grander scale, AI is poised to have a major effect on sustainability, climate change and environmental issues. Optimists can view AI as a way to make supply chains more efficient, carrying out predictive maintenance and other procedures to reduce carbon emissions.
At the same time, AI could be seen as a key culprit in climate change. The energy and resources required to create and maintain AI models could raise carbon emissions by as much as 80 percent, dealing a devastating blow to any sustainability efforts within tech. Even if AI is applied to climate-conscious technology, the costs of building and training models could leave society in a worse environmental situation than before.
Accelerated Speed of Innovation
In a 2024 essay about the future potential of AI, Anthropic CEO Dario Amodei hypothesizes that powerful AI technology could speed up research in the biological sciences as much as tenfold, bringing about a phenomenon he coins “the compressed 21st century,” in which 50 to 100 years of innovation might happen in the span of five to 10 years. This theory builds on the idea that truly revolutionary discoveries are made at a rate of maybe once per year, with the core limitation being a shortage of talented researchers.
By increasing the cognitive power devoted to developing hypotheses and testing them out, Amodei suggests, we might close the time gap between important discoveries like the 25-year delay between CRISPR’s discovery in the ‘80s and its application to gene editing.
What Industries Will AI Impact the Most?
There’s virtually no major industry that modern AI hasn’t already affected. Here are a few of the industries undergoing the greatest changes as a result of AI.
AI in Manufacturing
Manufacturing has been benefiting from AI for years. With AI-enabled robotic arms and other manufacturing bots dating back to the 1960s and 1970s, the industry has adapted well to the powers of AI. These industrial robots typically work alongside humans to perform a limited range of tasks like assembly and stacking, and predictive analysis sensors keep equipment running smoothly.
AI in Healthcare
It may seem unlikely, but AI healthcare is already changing the way humans interact with medical providers. Thanks to its big data analysis capabilities, AI helps identify diseases more quickly and accurately, speed up and streamline drug discovery and even monitor patients through virtual nursing assistants.
AI in Finance
Banks, insurers and financial institutions leverage AI for a range of applications like detecting fraud, conducting audits and evaluating customers for loans. Traders have also used machine learning’s ability to assess millions of data points at once, so they can quickly gauge risk and make smart investing decisions.
AI in Education
AI in education will change the way humans of all ages learn. AI’s use of machine learning, natural language processing and facial recognition help digitize textbooks, detect plagiarism and gauge the emotions of students to help determine who’s struggling or bored. Both presently and in the future, AI tailors the experience of learning to student’s individual needs.
AI in Media and Journalism
Journalism is harnessing AI too, and will continue to benefit from it. One example can be seen in The Associated Press’ use of Automated Insights, which produces thousands of earning reports stories per year. But as generative AI writing tools such as ChatGPT enter the market, questions about their use in journalism abound.
AI in Customer Service
AI in customer service can provide the industry with data-driven tools that bring meaningful insights to both the customer and the provider. AI tools powering the customer service industry come in the form of chatbots and virtual assistants.
AI in Transportation
Transportation is one industry that is certainly teed up to be drastically changed by AI. Self-driving cars and AI travel planners are just a couple of facets of how we get from point A to point B that will be influenced by AI. Even though autonomous vehicles are far from perfect, they may one day ferry us from place to place.
Risks and Dangers of AI
Despite reshaping numerous industries in positive ways, AI still has flaws that leave room for concern. Here are a few potential risks of artificial intelligence.
Job Losses
Between 2023 and 2028, 44 percent of workers’ skills will be disrupted. Not all workers will be affected equally — women are more likely than men to be exposed to AI in their jobs. Combine this with the fact that there is a gaping AI skills gap between men and women, and women seem much more susceptible to losing their jobs. If companies don’t have steps in place to upskill their workforces, the proliferation of AI could result in higher unemployment and decreased opportunities for those of marginalized backgrounds to break into tech.
Human Biases
The reputation of AI has been tainted with a habit of reflecting the biases of the people who train the algorithmic models. For example, facial recognition technology has been known to favor lighter-skinned individuals, discriminating against people of color with darker complexions. If researchers aren’t careful in rooting out these biases early on, AI tools could reinforce these biases in the minds of users and perpetuate social inequalities.
Deepfakes and Misinformation
The spread of deepfakes threatens to blur the lines between fiction and reality, leading the general public to question what’s real and what isn’t. And if people are unable to identify deepfakes, the impact of misinformation could be dangerous to individuals and entire countries alike. Deepfakes have been used to promote political propaganda, commit financial fraud and place students in compromising positions, among other use cases.
Data Privacy
Training AI models on public data increases the chances of data security breaches that could expose consumers’ personal information. Companies contribute to these risks by adding their own data as well. A 2024 Cisco survey found that 48 percent of businesses have entered non-public company information into generative AI tools and 69 percent are worried these tools could damage their intellectual property and legal rights. A single breach could expose the information of millions of consumers and leave organizations vulnerable as a result.
Automated Weapons
The use of AI in automated weapons poses a major threat to countries and their general populations. While automated weapons systems are already deadly, they can also fail to discriminate between soldiers and civilians. Letting artificial intelligence fall into the wrong hands could lead to irresponsible use and the deployment of weapons that put larger groups of people at risk.
Superior Intelligence to Humans
Nightmare scenarios depict what’s known as the technological singularity, where superintelligent machines take over and permanently alter human existence through intentional harm or eradication. Even if AI systems never reach this level, they can become more complex to the point where it’s difficult to determine how AI makes decisions at times. This can lead to a lack of transparency around how to fix algorithms when mistakes or unintended behaviors occur.
“I don’t think the methods we use currently in these areas will lead to machines that decide to kill us,” said Marc Gyongyosi, founder of Onetrack.AI. “I think that maybe five or 10 years from now, I’ll have to reevaluate that statement because we’ll have different methods available and different ways to go about these things.”
Notable AI Milestones
Here are a few key milestones in AI history that have shaped what the technology is today — and what it could become in the future.
GPT‑5 Release (August 2025)
OpenAI launched GPT‑5, introducing enhanced contextual understanding and sharper generative capabilities powered by expanded training data and optimized model architecture. GPT-5 represents yet another leap forward in benchmark-setting performance that broadly influences development across industries.
First Global AI Safety Summit Held (November 2023)
The first global AI Safety Summit convened at Bletchley Park in England, signifying a moment of reckoning for AI’s trajectory in the public and policy arenas. It marked the first time 29 nations — including the United States, China and the European Union — joined forces with a joint declaration on international AI safety cooperation.
This event elevated ethical AI governance into global diplomatic discourse. Hosted at the historic home of wartime codebreaking, the summit symbolized how AI’s future must be shaped with the same urgency and cooperation as past technological milestones.
ChatGPT Debuts (November 2022)
OpenAI launched ChatGPT, a large language model chatbot that quickly garnered massive public attention for its conversational fluency and broad utility — whether aiding with code, writing or research tasks. This launch matters as a significant moment in AI’s public adoption, and highlighted initial misconceptions, strengths and limitations of generative models.
Transformer Architecture Introduced (June 2017)
In 2017, researchers at Google published “Attention Is All You Need,” which introduced the transformer architecture — a foundational breakthrough enabling AI systems to model long-range dependencies in data more effectively than ever before. This marked a major milestone in AI development, as transformers underpin nearly all modern generative models, including those powering tools like ChatGPT, Google Gemini, Claude and more.
Deep Blue Defeats Chess Champion Garry Kasparov (May 1997)
In 1997, IBM’s Deep Blue became the first computer to defeat a reigning world chess champion, Garry Kasparov. This mattered because it demonstrated AI’s capacity to master complex, strategic tasks under human-level performance in a high-stakes domain.
First Trainable Neural Network Demonstrated (1957)
The first trainable neural network, known as Perceptron, was demonstrated by Cornell University psychologist Frank Rosenblatt in 1957. The Perceptron model was a single-layer neural network with adjustable weights and thresholds placed between input and output layers, mirroring modern neural network designs.
“Artifical Intelligence” Is Coined (Summer 1956)
In the summer of 1956, the Dartmouth Summer Research Project on Artificial Intelligence convened, where the term “artificial intelligence” was coined by John McCarthy, alongside key figures like Marvin Minsky, Claude Shannon and Nathaniel Rochester. This workshop laid the symbolic foundation of AI as a formal research discipline.
Alan Turning Introduces the Turing Test (1950)
In 1950, Alan Turing published “Computing Machinery and Intelligence,” introducing the concept of the Turing Test — a philosophical and practical measure of machine intelligence — and launching serious debate on whether machines could think.
What does the future of AI look like?
AI is expected to improve industries like healthcare, manufacturing and customer service, leading to higher-quality experiences for both workers and customers. However, it does face challenges like increased regulation, data privacy concerns and worries over job losses.
What will AI look like in 10 years?
AI is becoming a bigger part of daily life, with generative AI tools already helping people write, code and learn, and AI systems being used to analyze data and assist in research in almost every industry. In the future, AI could also further assist with human care, household tasks and workplace safety — boosting productivity and efficiency across different settings.
Is AI a threat to humanity?
Whether AI is a threat to humanity depends on how people in control of AI decide to use the technology. If it falls into the wrong hands, AI could be used to expose people’s personal information, spread misinformation and perpetuate social inequalities, among other malicious use cases.
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