What is Generative AI?

Since its launch, ChatGPT has generated awe and inspiration from its human-like text responses. The platform delivers value to anyone seeking advice, entertainment, or information in a conversational mode of exchange—and its potential for disrupting the future sparks curiosity from many. Yet, ChatGPT is one of many generative AI platforms unlocking opportunities and shifting the tech space known today.

The era of generative AI is just beginning. Excitement over this technology is palpable, and early pilots are compelling.”—McKinsey

Learn about generative AI, its market outlook, and current popular generative AI platforms that are ready to be used by society’s next change-makers.

What is Generative AI?

Generative AI is a type of artificial intelligence that generates content. It’s easy and automatic; provide a prompt to AI, and it will generate guides, calculations, and stories. It can create “realistic images for video games, musical compositions, and poetic language, using only text prompts…it can aid complex design processes, such as designing molecules for new drugs or generating programming codes” (gao.gov).

Since its introduction, useful AI applications are expanding both in number and capability. Generative AI is a breakthrough technology, but its true impact comes from what the next leaders and creators do with it.

Popular Generative AI Tools You Can Use Today

Showcasing eight generative AI platforms: ChatGPT, Gemini, AlphaCode, Microsoft Designer, GitHub Copilot, Scribe AI, Jasper, and Synthesia.

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Generative AI Market Outlook

Generative AI is currently used in various industries—healthcare, IT, education, robotics, banking, law, and finance, to name a few. Because of its ability to automate cognitive functions and intellectual tasks, generative AI can also streamline the corporate workplace. The technology’s use cases will vary from industry to industry, but its multi-industry applicability positions the generative AI market for rapid growth:

“The largest drivers of incremental revenue will be generative AI infrastructure as a service ($247 billion by 2032) used for training LLMs [Large Language Models], followed by digital ads driven by the technology ($192 billion) and specialized generative AI assistant software ($89 billion). On the hardware side of this, revenue will be driven by AI servers ($132 billion), AI storage ($93 billion), computer vision AI products ($61 billion) and conversational AI devices ($108 billion).”—Bloomberg

Despite its positive market outlook, it is also important to discuss the global economic outlook and the potential repercussions of introducing generative AI. At a smaller scale, the income gap within nations can increase: “AI could also affect income and wealth inequality within countries. We may see polarization within income brackets, with workers who can harness AI seeing an increase in their productivity and wages—and those who cannot falling behind” (imf.org). At a larger scale, generative AI has the capability of deepening the digital divide between nations: “emerging market and developing economies face fewer immediate disruptions from AI. At the same time, many of these countries don’t have the infrastructure or skilled workforces to harness the benefits of AI, raising the risk that over time the technology could worsen inequality among nations” (imf.org). 

Discussing the Duality of Generative AI: Promise and Peril

Generative AI is an ever-evolving landscape, filled with unprecedented possibilities and challenges. In the crossroads of this transformative era, it is ever so crucial to strike a balance between fostering technological innovation and mitigating risks.

Promise

As generative AI platforms advance over time, they will transition from platforms used to create solutions to platforms used to develop entire products. Indeed, in the years that come, engineers will develop numerous generative AI models—both as employees contributing to their company’s products and as entrepreneurs jumpstarting new businesses altogether from tapping into generative AI’s potential.

Currently, generative AI platforms such as ChatGPT pull information from the internet and relay that to users in a meaningful way. In the future, these platforms will apply enhanced reasoning to deliver niche solutions and “real-world applications, such as drug design, material engineering, personalized medicine, and protein biochemistry” (doi.org).

Already, new version ChatGPT-4 can analyze and respond to image queries, generate code for more complex programs based on natural language, pass difficult exams, remember and respond to larger context, and understand human sentiment and humor better (engage-ai.co).

Generative AI will also grow to complement employee work by automating basic tasks such as data entry, as well as entire email and presentation deck generation. As a result, employees are granted time for more valuable work, including client facing tasks and strategic planning. It is also expected to integrate with key workflow applications, like Microsoft Office suite software (i.e. Outlook, Excel, OneNote, PowerPoint) and Google apps (i.e. Gmail, Sheets, Docs, Slides). 

“AI can now ‘join’  meetings and provide summaries of meetings and assign action items. Before committing to watching a long video or reading a paper, AI can provide a short recap, enabling people to determine whether they want to invest the time to view it in its entirety. When reading a book, people can’t remember every detail, but AI could supplement human memory and quickly find desired passages” (uc.edu).

Generative AI Use Cases for Employees in the Workplace

The advent of generative AI technology is an invaluable asset for businesses aiming to stay competitive in an increasingly digital world, aiding in tasks from drafting personalized marketing content to designing product prototypes. Its incorporation in companies calls for employees to anticipate its day-to-day impact and take charge of this

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Peril

The promise of future opportunities aside, the risks associated with generative AI are impossible to ignore. The premonition surrounding generative AI from both consumers and businesses cannot be attributed to one cause. An article published by HBR delves into multiple concerns that deter progress towards total adoption: “disinformation, safety and security, the black box problem [or, the uncertainty behind why generative AI produces the output that it does], ethical concerns, bias, instability, hallucinations in LLMs, unknown unknowns, job loss and social inequalities, environmental impact, industry concentration, state overreach.”

Other concerns include its potential to create deepfakes, misinformation, ambiguity with copyright and intellectual property, or leak confidential information—thereby eroding trust in digital media (ft.com). 

Fears surrounding deepfakes are well-grounded, most alarming when the technology misrepresents and seeps into political campaigns, power figures, and others with vast public reach or influence. For those curious, visit WSJ’s experiment featuring Joanna Stern and her AI clone. To demonstrate, view below a clip of the real Anderson Cooper versus his deepfake counterpart.

In the wrong hands, the technology can generate detrimental new ideas, like the code for malicious software or even the molecular design for dangerous drugs: “researchers used AI to suggest 40,000 new potential chemical weapons in six hours… these researchers were using AI to find vaccines but, instead, realized they could create viruses (uc.edu). 

These threats have sparked debates about the need for government regulation to safeguard against misuse, consequently creating a bottleneck in the advancements of generative AI.

Get Involved with Generative AI—Conclusion

This era of artificial intelligence represents a quantum leap in design evolution, empowering creators with its innovative approach to visual aesthetics. It is clear that generative AI provides value for all its adopters - at home users, employees, businesses, decision-makers in organizations, and policymakers. Still, generative AI’s potential is largely untapped.

For good or for worse, users will govern the outcome of generative AI largely because the use cases and prompts that users input daily help train generative AI. Therefore, users must do their due diligence to consider dangers and unwanted outcomes of AI—instead using the technology to incite beneficial change.

It is time for creators, coders, entrepreneurs, and leaders to take charge of generative AI to generate value for society.