Generative AI: What It Is, How It Works, and Its Current Impact

What has happened?

In recent years, generative artificial intelligence (GenAI) has gone from a lab concept to an everyday reality. Tools like ChatGPT (launched in November 2022), Microsoft Copilot (integrated into Office 365 in March 2023), Gemini (formerly Bard, by Google), and image generators such as DALL-E 3 (OpenAI) and Midjourney have achieved mass adoption. According to Zapier, if you've used a chatbot or an image generator, you've already used generative AI. This progress has been made possible by improvements in computing power, especially with GPUs like the NVIDIA A100 and H100, and the development of large language models (LLMs) such as GPT-4, Claude 3, and LLaMA. GenAI is not an isolated phenomenon: it is the result of decades of research in deep learning, neural networks, and natural language processing. Key milestones include the Transformer architecture (2017), the GPT-2 model (2019), and the explosion of ChatGPT's popularity, which reached 100 million users in two months.

Why is it important?

Generative AI represents a qualitative leap compared to traditional AI. While earlier systems were limited to classifying (like spam filters) or predicting (like recommendation engines), GenAI can create new content: text, images, music, code, and even video. This opens up enormous possibilities in automation, creativity, and productivity. For businesses, it means cost and time reductions in tasks such as writing, design, or customer service. For example, according to a McKinsey study, GenAI could add between $2.6 and $4.4 trillion annually to the global economy. For users, it offers intelligent personal assistants and accessible creation tools, such as text-to-image generation in DALL-E or music composition with Suno AI. However, this power comes with risks: the ability to generate realistic content can also be used for disinformation or impersonation.

Consequences and challenges

The impact of GenAI is profound and multifaceted. In the workplace, it automates repetitive tasks but also raises concerns about job losses in sectors like writing, translation, graphic design, and customer service. A Goldman Sachs report estimates that 300 million jobs could be affected by automation, though new roles will also be created. Ethical issues arise, such as biases in models (e.g., GPT-4 showed racial and gender biases in studies), disinformation (political deepfakes like the fake Biden audio in 2024), and copyright of generated content (lawsuits from authors and artists against OpenAI and Stability AI). Regulations like the EU AI Act, approved in March 2024, aim to mitigate these risks by classifying AI systems according to their risk level. Additionally, the energy consumption of large models is a growing environmental concern: training GPT-3 consumed approximately 1,300 MWh of electricity, equivalent to the annual emissions of 130 U.S. households.

What readers should know

Generative AI is not a passing fad: it is a technology here to stay. To leverage it without risks, it is crucial to understand its limitations (hallucinations, lack of common sense, dependence on training data) and use it as a support tool, not as a source of absolute truth. Businesses must invest in training and ethics, while users should maintain critical thinking and verify generated information. The future points to more efficient models (such as smaller, specialized models), multimodal ones (combining text, image, audio, and video), and personalized systems integrated into all aspects of digital life, from virtual assistants to advanced recommendation systems. The key will be finding a balance between innovation and regulation to maximize benefits and minimize harms.