The internet was created in the 1960s for inter-agency government, scientific, and university communications. However, it was popularized in the early 1990s thanks to the launch of the Mosaic browser in 1993. From 1993 to 1996, the number of websites skyrocketed from 130 to over 100,000, with the most popular web browser, Netscape Navigator, generating more than 10 million global users. According to the consulting firm McKinsey and Company, by 2011, the internet sector would have a greater impact or weighting in GDP than energy, agriculture, and utilities. In fact, research done by the Brookings Institute in 2001, showed a slowdown in global productivity growth from 1973 to 1995 and then a dramatic increase from 1995 to 2000. The reason? The internet.
Today, thanks to the connectivity, the internet has evolved into a vast ecosystem, where adoption and speed are key metrics to growth and profitability – many of which are social in nature. The two fastest adoption technology Apps that generated 100 million users, were done by Instagram at two and a half years and now TikTok, which took nine months (Hu, 2023). However, when ChatGPT was released in November of 2022, it took only two and a half months for them to reach 100 million users.
With record-breaking statistics like these, individuals from all walks of life are left wondering: What does this mean for me? How will it affect my job? The rapid growth and widespread adoption of generative AI technology within such a short period of time has made the future difficult to predict. And just like the fervor of the internet, the nuance of generative AI’s capabilities is both exciting and scary, as it opens new possibilities and raises ethical concerns. Below, we explore its potential implications and understand how it can help us navigate the changing landscape of the future.
What is Generative AI?
Generative AI is a user interface that has access to a vast amount of data that interacts with users in a human-like way, providing simple or complex answers to user questions. The way that Alan Gurung, CEO of a generative AI startup SIFA, explained this to me was that “it is like telling a story to a friend of yours who is listening very closely, but you forget what you are going to say next. Because your friend was listening so intently, they can make a good assumption of what you are going to say next without you having to say it. If your friend knows you well enough and has heard enough stories about you, over time, they will get more and more accurate at predicting what you are going to say next.”
There are tons of generative AI systems available now. So, what makes them different? The first thing is how they are trained (NVIDIA, 2023). The second difference can be seen in the outputs the system creates. DALL-E by OpenAI is trained to create an image based on any criteria you want. If you want a painting of “a cowboy playing the saxophone while riding a horse in the arctic” it will create a picture based on its own understanding of those inputs.
Instead of painting masterpieces, OpenAI’s ChatGPT focuses on following prompt instructions to give detailed responses while interacting with the user in a conversational way (OpenAI, 2022). These responses can range from relationship advice to historical essays. For example, I asked ChatGPT to name this wonderful painting and it gave me “Arctic Serenade: The Saxophone Cowboy.”
Generative AI has shown itself to be an innovation with a variety of uses and applications. The impact on industries has been heterogeneous. A compelling question hangs over employees and employers: “Can generative AI perform my job more effectively than I can?” and perhaps even more frightening, “When will generative AI surpass my own abilities in performing my job?” The answers to these questions will undoubtedly shape the future landscape of industries, raising concerns, possibilities, and considerations for professionals across numerous sectors.
Since the effects of the pandemic and the overall rising use of technology, telehealth has become an increasingly important aspect of the health industry. Telehealth has grown so much that as of April 2021, 84 percent of physicians were offering virtual visits and 57 percent would prefer to continue offering virtual care (McKinsey, 2021). This immense growth has doctors looking for AI assistants that would help them manage this workload. One possible solution that has arisen would be for clinics to use ChatGPT to respond to patients. In an investigative study by JAMA, they compared ChatGPT and physician responses to patient questions to see if ChatGPT would be capable of this task. The study found that the subjects considered ChatGPT’s responses as more empathetic, but this could be because ChatGPT averaged longer responses than the doctors. There was no evidence to conclude that one writing was more accurate than another, but it did show that AI could help improve responses in terms of empathy and thoroughness that a full-time doctor might not have time to write.
Beyond responding to patient inquiries, generative AI could be used for medical notetaking and drug discovery. Technology giant Microsoft and health software company Epic are partnering to integrate “Microsoft’s Azure OpenAI service into Epic’s EHR software” (Halleman, 2023). Physicians could benefit from this integration by using generative AI to help them automate notetaking and then seamlessly transfer information directly into their EHR (electronic health record) system. Generative AI has also been deployed in drug discovery due to its impressive ability to recognize patterns in massive datasets. This can be seen through a trained AI’s ability to use chemical properties to identify new candidates possessing similar properties with distinct structures that have the potential to yield safer and more effective drugs (Forbes 2023). This application could be so dynamic that Gartner predicts that “by 2025 generative AI will be used by 50% of drug discovery and development initiatives” (Forbes 2023). Due to the many problems within healthcare that can be aided or improved by generative AI, the healthcare industry is likely to face one of the most impactful disruptions by AI.
In a world where many services are offered at the touch of a button on your phone, seeking insurance can feel primitively difficult. Have you ever had to suffer through poorly designed websites or extended phone calls with an insurance company? These customer service pain points lead to problems in customer attraction and retention with one survey finding that “48% said a particular incident of poor service motivated them to switch providers” (Paau, 2022).
An independent software vendor, Zelros, has seen this deficiency as an opportunity by saying that they “see the limitations of deploying generative AI as being bound only by our ability to creatively reinvent the consumer experience for insurance customers everywhere” (Philippon, 2023). Zelros ultimately seeks to reach a point where customers can “ask a machine in plain, natural language whether a specific consumer situation is covered or not–and whether this policy is more suitable than the next” (Philippon, 2023). Zelros also plans on using generative AI by “tailoring messaging” to consumers based on their circumstances such as location, desires, and stage of life. These strategies will help insurance companies offer their services without the headaches of representatives on the phone. However, how willing are consumers to abandon human customer service for machines? In a survey of over 8,000 consumers across 16 countries, Salesforce found that “62% of customers are open to the use of AI to improve their experiences” (Featherstone, 2020). McKinsey & Company predicts that the next decade will shift further towards digital technology and give insurance carriers the opportunity for transformative approaches to overall business structures to improve productivity and reduce operational expenses by up to 40 percent (Erk, 2020). As a result, companies will seek out software solutions from companies like Zelros to stay competitive in this industry.
Software and Hardware Industries
Generative AI has the potential to both complement and accelerate the work of programmers. ChatGPT is proving itself to be capable of writing simple code and correcting bugs within existing code. Programmers are experimenting with ChatGPT to see if it can streamline processes and automate certain tasks within the coding process. Collaborative programming approaches are emerging where AI systems write portions of the code while human programmers validate and ensure its accuracy. This dynamic suggests that generative AI is not necessarily a replacement for programmers but rather a means of accelerating their work. However, this creates a new threat for programmers, Mark Muro, an AI researcher at Brookings Institute stated “What took a team of software developers might only take some of them” (Mok, 2023). Consequently, these efficient programming processes may lead to companies minimizing the number of programmers they hire to cut costs and maximize profitability.
The rise of generative AI is also expected to have a significant impact on the semiconductor industry. While other industries are seeing disruptions due to the applications of generative AI, the semiconductor industry is being affected because of generative AI’s operational necessities. Services like ChatGPT can be costly in terms of hardware. SemiAnalysis, a semiconductor research and consulting firm estimates that “ChatGPT costs $694,444 per day to operate in computer hardware costs” (Patel, 2023). That means that every time you ask ChatGPT to write your history report, it costs Microsoft a couple of cents. The increasing demand for computing hardware bodes well for the semiconductor industry as they are responsible for designing and producing computer chips used in all electronic devices. This simultaneously increases the need for hardware specialists who can develop and optimize chips to support the requirements of generative AI. NVIDIA, known for its expertise in graphics processing units (GPUs) used in AI applications has already seen an “added $245 billion to its stock market capitalization between May 24 and pre-market trading on May 30 — due to high demand for its GPUs” (Cohan, 2023). Companies like NVIDIA in this industry are playing an increasingly crucial role in providing the necessary hardware solutions for the generative AI era.
An unexpected industry under threat of replacement by generative AI is the creative industry. Some of the possible uses of AI in the creative can be seen through the acceleration of tasks such as idea development, animation, and script writing. Generative AI gives artists the ability to explore a multitude of concepts quickly and efficiently. AI tools have already been used to accelerate repetitive tasks in the film industry. In a Wall Street Journal interview, VFX artist Evan Halleck talked about how instrumental AI has been to accelerate a lengthy editing process used to add visual effects in a frame-by-frame manner called “rotoscoping” (Thomas, 2023). This is threatening for some VFX artists because Halleck “used to outsource this work to others who could make anywhere from 500 to over a thousand dollars a day, he says. Now, with AI tools, he doesn’t need to hire them” (Thomas, 2023). With AI already playing a part in modern films it would not be shocking to see generative AI start making advances in this industry.
Generative AI could also give rise to an entirely new form of art, where AI acts as the “creative copilot” to a human artist. OpenAI’s image generation tool, DALL-E, can give artists the ability to “generate images from a description, make targeted edits to images, and create different variations of an image” (OpenAI, 2022). This tool’s power can be seen as threatening by some. Getty Images (an online supplier of stock images) has banned AI-generated content due to concerns over copyright issues (Vincent, 2022). On the other side of the spectrum, Adobe has shown openness to accepting AI-generated content as long as it is declared as AI-created and meets submission standards (Adobe Help Center, 2023). This contrast is representative of generative AI’s overall effect on the creative industry right now; some people are excited to implement these powerful new tools, while others fear the unintended consequences the nuance of these implementations could have.
Current Applications for Business
As it stands today, Generative AI is an unprecedented technological advancement but how much faith should we put into its services? Sam Altman, CEO of OpenAI has said himself that “ChatGPT is incredibly limited, but good enough at some things to create a misleading impression of greatness. It’s a mistake to be relying on it for anything important right now. It’s a preview of progress; We have lots of work to do on robustness and truthfulness” (Altman, 2022). So, I won’t give AI the keys to my car, but I might use it to find the fastest way home.
One of generative AI’s first applications to business is through the advancements of chatbots. Have you ever used a website chatbot and received limited responses that leave you more frustrated than you started? That is because these traditional chatbots use rules, decision trees, and scripted responses to provide automated customer service (Gupta, 2022). However, a chatbot armed with generative AI can use highly developed programming and machine learning to deliver personalized recommendations to larger ranges of questions. Businesses with customer service departments are starting to experiment with the applications and benefits created by implementing these upgraded chatbots.
Many service businesses today outsource to call centers to handle their customer support. Call centers are already experiencing major pressure due to competition from chatbots. This pressure resulted in many businesses using chatbots for frequently asked questions and customer service representatives for complex requests. The combination of these two customer service options is best described as irritatingly difficult. Advances in machine learning have increased the pressure on call centers as chatbots are approaching the ability to completely automate customer service due to AI. Cristian Rennella, CEO and co-founder of a Colombian financial product comparator believes that “in two years we will be able to replace our entire call center with AI” (Roe, 2018). This advancement could lead to businesses no longer having to sacrifice a portion of their profits on outsourcing to call centers. Rennella decided to build his own chatbot which cost 8 months and $340,000 in developer salaries, but the result was an additional $3.7 million in additional annual revenue (Ismail, 2018). This opens up the opportunity for a developer to create a software as a service that provides businesses with an all-purpose customer support chatbot that requires little to no human interference. Given Rennella’s success, this service would be enticing to all businesses given that the costs are competitive with the overall expenses associated with call centers.
Marketing is another business aspect that could be greatly influenced by generative AI. Generative AI’s ability to analyze large amounts of information and give personalized responses makes it a great marketing tool. Public relations failures over the years have shown us that all publicity is not always good publicity, and AI probably knows this too. With carefully crafted prompts, marketers can use generative AI to develop headlines, blog posts, and social media posts that engage customers (Staff, 2023). Businesses can also use generative AI, like DALL-E, to create images desired in high-end marketing pieces that are visually appealing, engaging, and relevant to their audience.
Overall, marketing will see many new applications of generative AI in the future. As machine learning techniques advance, generative AI will extract its messages from increasingly larger databases. Once these databases incorporate massive amounts of customer preferences, feedback, demographics, and purchasing behavior, generative AI could be able to produce and personalize marketing content for target audiences more efficiently and effectively than human marketers.
In my search for information, I interviewed Alan Gurung who is the CEO of SIFA. SIFA is a generative AI startup that seeks to act as an assistant to financial advisors. One of Gurung’s visions for the future through SIFA is for advisors to not have to type anything into the computer. All information would be collected by the advisor through voice recordings and documents where generative AI would then transcribe and structure that information into what the financial advisor needs. With this automated process in place, Gurung offers that “An advisor would be able to spend all of his time speaking with clients and starting to generate more revenue at a lower cost while looking after more clients as well.” One impressive feature of SIFA’s AI is being able to learn to write emails using your style. After learning from fifty or so emails you have written, SIFA AI can be given prompts to write emails and function somewhat as a duplication of yourself. SIFA’s personalized time-saving functions are a perfect example of the benefits businesses will see with generative AI.
The Future of Generative AI
With all of these impressive technological advancements presented by generative AI, AI systems are poised to reshape the landscape of the workforce. While replacement by AI will be seen in repetitive and routine tasks, AI systems will not be taking our jobs in most cases but accelerating the quantity and quality of tasks we can complete. Most jobs are safe due to weaknesses in clarity, thoroughness, and accuracy found in current versions of generative AI. Content created will have to be reviewed and edited before it can be considered complete. Gurung explained that “AI won’t replace financial advisors, advisors who use AI will replace advisors who don’t.” I believe that this statement will be representative of generative AI’s impact across most job sectors. Those who cannot adapt to generative AI will fall behind those that can. Industry-specific AI tools Microsoft’s integration into electronic health, Zelros’ reshaping of insurance service, and SIFA’s automation of financial advising will challenge people to collaborate with AI and adjust their processes. These innovations will lead to creativity, empathy, and critical thinking to be considered as significantly more valuable. Reasonable regulations will have to be put in place to protect users from misinformation and privacy risks as AI evolves and usage increases. With more use cases and applications being discovered each day, generative AI will be one of the most utilized tools throughout the world in the near future.
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