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.
How Will AI Impact the World?
Artificial intelligence is a powerful tool capable of automating work and accelerating scientific discovery. As it advances and becomes more useful, AI will have a wide variety of impacts on the world, both good and bad, ranging from environmental degradation as well as more efficient hybrid workforces.
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
Current Impact
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.’”
Future Outlook
As artificial intelligence becomes more powerful over the next few years, it will likely handle more tasks previously performed by human workers. In particular, advancements in AI agents will enable people to hand off more complex tasks to automation.
Businesses may transition from a model of human-led workflows to a hybrid workforce where humans act as orchestrators for AI agents. In these types of roles, employees will simply describe intent and command their agents to work across software to deliver the final result instead of manually navigating multiple apps to complete a project.
Job Disruption
Current Impact
Business automation has naturally led to fears over job losses. Although AI has made gains in the workplace, it’s had a massive impact on different industries and professions. 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.
Despite the concern about mass unemployment brought upon by AI, the job market has only seen minimal impact since 2022, the year ChatGPT became publicly available. According to ADP research, AI has mainly affected early-career workers in fields with high exposure to AI, such as software engineering and customer service. According to the data, employment for 22- to 25-year-olds in high AI exposure roles fell by 6 percent between 2022 and 2025.
However, during that same time, employment for workers 30 and older in those same fields increased by 13 percent. The divide is likely due to AI’s current inability to automate more complex tasks and work that more experienced workers would otherwise carry out. Because of such limitations, AI will likely have an uneven impact on the labor force. For example, AI is already automating repetitive jobs; meanwhile, creative positions are more likely to have their jobs augmented by AI, rather than outright replaced. But the demand for other jobs like machine learning specialists and data center technicians has risen.
Future Outlook
While the future impact of AI on the job market may largely depend on its technical limitations, The World Economic Forum describes a possible scenario by 2030 where exponential AI advancements outpace workers’ ability to reskill, forcing companies to further automate roles, leading to unemployment spikes. Less ominous viewpoint points predict around one in four jobs will likely be transformed over the next five years.
“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.
While AI could displace as many as 92 million jobs by 2030, the World Economic Forum's Future of Jobs report suggests a net positive outcome, with the creation of 170 million new roles. These new positions will likely center on "human-plus" capabilities: AI ethics officers, human-AI collaboration designers, and specialized roles in physical AI, such as robotics and autonomous mobility.
Data Privacy Issues
Current Impact
LLMs work by scraping vast amounts of data from a large number of sources, including websites, forums, social media and much more. Even though some AI companies have agreements with certain platforms to train on their data, individuals whose information might be included in those agreements have no say on how their personal data is used. And there is no way for individuals to request that their data be deleted from an LLMs training material.
Some experts are concerned that by using personal data, AI systems are able to infer sensitive information such as sexual orientation, political views or health status, and that this could lead to algorithmic bias or the stereotyping of certain groups. Researchers have already widely documented this issue. For example, there have been incidents where AI systems reinforce gender roles or AI image generators overwhelmingly create images of white men when prompted to show an image of a successful person.
Future Outlook
By 2030, the reliance on raw, scraped internet data is expected to diminish, replaced by synthetic data — artificially generated information that mimics real-world patterns without exposing personal identities. Experts predict that by 2026, nearly 60 percent of all AI training data could be synthetically produced, significantly lowering the legal and ethical risks associated with unauthorized data harvesting.
During that same time, the AI industry may adopt a privacy by design approach to data collection. This framework integrates privacy protections directly into the initial engineering of AI systems rather than treating them as an afterthought or a secondary layer of compliance. By embedding safeguards like data minimization and end-to-end encryption into the software’s architecture, companies can ensure that personal information is automatically protected.
Increased Regulation
Current Impact
AI could shift the perspective on certain legal questions, depending on how various generative AI lawsuits continue to unfold. 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.
Future Outlook
In the United States, the federal government’s hands-off approach aims to prioritize national dominance and infrastructure growth over regulatory oversight, even blocking states from imposing regulations. However, this federal leniency may trigger a legal battle with states like California, New York, Illinois and Texas, which may continue to push for their own protections regarding consumer privacy and algorithmic bias.
Internationally, the EU AI Act will reach full implementation by August 2026, setting a global benchmark for high-risk AI systems. As AI agents become more autonomous, the focus of regulation will likely shift from how developers train models to how those models behave in the real world. We may see the introduction of AI liability when an autonomous system makes a costly or harmful error.
Climate Change Concerns
Current Impact
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. But despite its potential benefits, AI is also 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. For example, GPT-4, a now defunct LLM, is estimated to have consumed 50 gigawatt-hours of energy during its training phase, which is enough electricity to power the entirety of San Francisco for three days, according to MIT Technology Review. At the same time, the data centers that train LLMs can use as much as 5 million gallons of water per day to cool its hardware. That amount is equivalent to the needs of a town with a population between 10,000 and 50,000 residents.
Future Outlook
With AI’s current energy and water demands, outlooks for the tech’s impact on climate change are not optimistic. The World Economic Forum estimates that AI could add between 0.4 to 1.6 gigatonnes of carbon dioxide equivalent annually by 2035. And as for data centers, Goldman Sachs predicts they will be responsible for up to 4 percent of the global energy usage.
To mitigate this, the next decade could see a massive push toward decentralized data centers and edge-enabled chips specifically designed for edge AI, which processes data locally on devices rather than in massive, water-cooled server farms. Major tech firms are already pivoting toward nuclear energy to meet these demands; by 2030, small modular reactors (SMRs) may become a standard power source for the industry's largest training clusters.
Accelerated Speed of Innovation
Current Impact
AI has quickly become the operational backbone of scientific research. In 2026 autonomous systems that can plan and execute experiments rather than just summarize data and are shortening research and development cycles. Pharmaceutical leaders are now using AI to design completely new antibiotics from scratch and predict the toxicity of compounds before they ever enter a physical lab. Elsewhere, companies like CATL are using AI-powered platforms to cut prototype development for EV batteries by nearly 46 percent, processing millions of data records to deliver optimal designs in minutes rather than weeks.
Future Outlook
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 1980s 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.
AI in Software Engineering
AI has significantly changed software engineering though generative chatbots and coding-specific tools like Claude Code and Cursor. These platforms have enabled engineering teams to boost productivity by automating code generation, with software developers acting more like orchestrators by reviewing AI-generated code. This shift has led to a significant decrease in the number of software development job posts, along with an 8 percent decrease in undergraduate enrollment in computer and information science programs.
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.
Frequently Asked Questions
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.
