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Explore the benefits and drawbacks of generative AI. How will it transform the way companies work?
- Generative AI tools reduce the money and time needed for content creation, thereby boosting productivity and profitability.
- The rise of generative AI also breeds innovation, paving the way for new business models and applications.
- However, generative AI could lead to job displacement in fields such as programming and copywriting.
- There are also copyright issues and data security risks involved, especially where client confidentiality is concerned.
Disclaimer: This article was not written using generative AI.
Generative AI — a category of artificial intelligence algorithms that can generate new content based on existing data — has been hailed as the next frontier for various industries, from tech to banking and media. And indeed, we’re already seeing the many ways the technology is being adopted.
In 2022, a deep learning model called DALL-E 2 made headlines for its ability to generate digital images from text prompts. More recently, ChatGPT has taken the world by storm thanks to its advanced conversational capabilities. The brainchild of OpenAI, the AI-powered chatbot is backed by investors including Microsoft. The latter is integrating the technology into its Bing search engine and Edge browser, and eventually plans to build it into “every layer of the stack,” as CEO Satya Nadella announced.
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In March 2023, OpenAI unveiled the latest iteration of its ChatGPT software, GPT-4, which is currently available to subscribers and developers. Rival firms are hoping to launch similar tools: For instance, Alphabet is working on a conversational AI service called Bard.
“Incorporating the full power of ChatGPT into search and browsing will create tangible value for consumers, enabling richer responses to queries that go beyond simply returning a list of links. This also creates a virtuous cycle of better engagement for consumers and higher-value targeted ads for advertisers, ultimately resulting in fewer ads overall to the benefit of both parties,” said Mark Murphy, Head of U.S. Enterprise Software Research at J.P. Morgan. “While Microsoft’s AI initiatives are clearly still early-stage, we believe a paradigm shift is underway.”
Besides enhancing the search experience for consumers, generative AI also has myriad implications for businesses — both positive and negative. Read on to find out how the technology could transform the way companies work.
What is generative AI?
Generative AI refers to a category of artificial intelligence algorithms that can generate new content based on the data they have been trained on. Their output — which includes text, images, videos, audio and more — typically resembles human-generated data.
What are some examples of generative AI?
ChatGPT, an artificial intelligence chatbot, is a form of text-based generative AI. Other popular examples include DALL-E 2, which can generate digital images from natural language descriptions.
What are the impacts of generative AI?
Generative AI reduces the money and time needed for content creation. It could also breed innovation, paving the way for new business models and applications. However, the technology could displace jobs and lead to copyright issues and data security risks.
What are the advantages of generative AI?
“Fundamentally, generative AI reduces the money and time needed for content creation — across text, code, audio, images, video and combinations thereof,” said Gokul Hariharan, Co-Head of Asia Pacific Technology, Media and Telecom Research at J.P. Morgan. Businesses can produce more content at speed and at scale, boosting productivity and profitability.
Generative AI could revolutionize content creation
The rise of generative AI could also breed innovation, paving the way for new business models and applications. While tools like ChatGPT are trained on generic data, there could soon be generative AI systems designed for specific verticals and datasets, be it market intelligence or medical research. “ChatGPT is putting the wind in the sails of other companies, and hundreds of new startups are rushing to develop foundation models, build AI-native apps and stand up infrastructure,” said Hariharan. “Because of the potentially very large impact, a positive sentiment cycle here could well lead to a valuation bubble in related names.”
Hardware companies, especially those producing memory chips, also stand to benefit from the uptake of generative AI technology. “AI computing workload has been doubling every three to four months from 2012 onward, and is likely to accelerate further. We expect the growing adoption of generative AI to spur demand for AI computing hardware in the next several years,” said Hariharan.
It’s critical that generative AI is used responsibly and governed properly, so that it can amplify human potential instead of becoming too disruptive.Mark Murphy,
Head of U.S. Enterprise Software Research, J.P. Morgan
What are the disadvantages of generative AI?
On the flip side, the rise of generative AI could affect jobs as machines begin to supplant human workers. “AI could lead to further job displacement for those engaged in the processes impacted, and in some cases companies and business models may become obsolete,” said Hariharan.
For instance, generative AI’s ability to crunch numbers and write code could impact programmers in the tech industry, where Big Tech firms are already downsizing their workforces in a bid to optimize costs. “However, there’s currently a shortage of software developers, so if you could have generative AI help write code, that’s solving a major economic bottleneck,” noted Murphy.
Likewise, the technology has proven proficient in generating text, which could put jobs such as copywriting and customer service at risk. For example, Microsoft is launching a new AI assistant called Dynamics 365 Copilot, which will be able to draft contextual responses to customer queries, write product listings for e-commerce websites and more.
But generative AI tools are not 100% accurate — at least, for the time being. ChatGPT is prone to “hallucinations,” or output that deviates from its training data. “Because of this, generative AI will not yet replace jobs entirely. Instead, it will augment existing jobs by automating repetitive tasks, freeing up time for workers to do other things,” said Hariharan.
Generative AI tools also run the risk of plagiarism and copyright infringement, as they often repeat or paraphrase data sourced from elsewhere on the Internet. Then there are the data security risks involved, especially where client confidentiality is concerned. When new information is inputted into a generative AI system, it becomes part of its data repository and is made publicly available to other users. “Companies will understandably be guarded about this, so generative AI providers will need to create tools that are ringfenced — ensuring that all information is self-contained to each organization and isn’t comingled with the rest of the world,” said Murphy.
In addition, generative AI is expensive to deploy, which could limit its uptake. “Right now, a lot of the buzz surrounding generative AI is fueled by curiosity. But the challenge is that the tools are quite expensive as a lot of cloud computing and hardware are required to run the algorithms,” noted Hariharan. “Generative AI won’t be easy to commercialize unless you have very strong demand drivers that are monetizable. The results have to justify the cost of usage.”
Overall, despite these hurdles, generative AI could well be a game changer for business, dramatically redefining the way companies work. “Generative AI is the most important technological development of the last several decades. It is rapidly enabling use cases and scenarios that people once said would be impossible to achieve, and it’s only going to get smarter,” said Murphy. “It’s critical that generative AI is used responsibly and governed properly, so that it can amplify human potential instead of becoming too disruptive.”
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