The newest unceasing and modern technological advancements are led by domains like artificial intelligence (AI), robotics, blockchain, and programmable biology. These technologies are revolutionizing retail, automobile, finance, manufacturing, and lots of more industries on each, macro and micro levels.

AI, specifically generative AI, is transforming the lifestyles and day-to-day tasks of information staff – individuals which might be material experts with formal education and training. Quite evident in professions comparable to programming, designing, engineering, and writing, generative AI has enhanced the productivity of information staff.

But what’s generative AI exactly and what makes it critical for knowledge staff? Let’s explore this concept more! 

What’s Generative AI?

Generative AI creates recent content comparable to text, video, audio, and image mechanically using AI algorithms, based on human-written prompts. 

A number of the most outstanding AI generation tools and products include:

• ChatGPT – Developed by OpenAI, ChatGPT is an intelligent AI chatbot able to providing extremely detailed and personalized responses based on user prompts.
• DALL-E 2, Stable Diffusion, & Midjourney – These are AI-powered image generation tools.
• Meta – That is an AI-powered video generation tool that enables users to generate videos from textual prompts.
• Codex – It enables programmers to generate code in several programming languages inside a number of seconds.

Now, let’s see how generative AI affects knowledge staff!

Understanding How Generative AI Increases the Productivity of Knowledge Employees From Different Domains

In accordance with ARK’s Big Ideas 2023 report, AI is anticipated to extend the productivity of information staff greater than 4-fold by 2030. The report also suggests that with 100% adoption, AI could herald roughly $200 trillion by way of labor productivity after an overall AI spend of $31 trillion. If vendors can extract merely 10% of the worth created by their AI-based products, they will collect nearly $14 trillion in revenue and $90 trillion in enterprise value in 2030.

AI market forecast for 2030. Source: ARK’s Big Ideas 2023

Let’s see intimately how AI generator tools contribute to increasing the productivity of content writers, developers, and artists.

1. Knowledge Employees: Content Writers & Editors

Modern businesses need well-researched and elegantly crafted content to draw audiences. That is where generative AI makes the job of content writers and editors easier.

With the emergence of intelligent chatbots comparable to ChatGPT, content creation is becoming increasingly easy and economical. In accordance with ARK’s Big Ideas 2023  report, ChatGPT’s per query inference, costs around $0.01 in 2022. For a billion queries, the entire inference cost becomes $10,000,000. By 2030, this cost is anticipated to shrink to only $650, based on Wright’s law. 

A value decline of this magnitude would enable the mass adoption of AI content tools. For example, by 2030, ChatGPT-style applications are anticipated to match Google Search’s scale and process 8.5 billion searches every day. Hence, it is going to turn out to be easier for knowledge staff within the content domain to leverage generative AI in on a regular basis tasks.

2. Knowledge Employees: Software Engineers & Developers

Given the complex and long software development cycles, managing and deploying software requires a team of dedicated, expert developers and programmers. Generative AI coding tools like Codex and Copilot are making software development easier and more productive for knowledge staff. 

Actually, ARK’s Big Ideas 2023 report states that AI coding assistants reduce the time to finish a coding task by half. By 2030, AI coding assistants could increase the output of software engineers by 10-folds.  

Time to finish coding tasks. Source: ARK’s Big Ideas 2023

3. Knowledge Employees: Visual Artists & Designers

One other group of information staff categorized as artists and designers can also be influenced by generative AI. Their tasks often include creating visual concepts, graphics, illustrations, and artistic UIs using designing tools like Adobe Photoshop, Illustrator, and Canva to deliver wealthy user experiences. 

With ground-breaking generative image models like DALL-E2, Stable Diffusion, and Midjourney, the productivity of designers has increased immensely. For example, graphic designs made by humans in 5 hours and costing $150 can now be effortlessly made in under a minute for 8 cents using generative image models. 

4. Knowledge Employees: Musicians & Sound Engineers

Generative AI makes composing and mixing a musical track much easier. For example, Google’s AudioLM is a generative audio model that makes realistic piano music and completes incomplete acoustic tones. Google has also developed a music generation model named MusicLM that may generate beautiful melodies based on text descriptions.

Back in 2020, Open AI introduced the same music generation tool generally known as Jukebox that generates a recent music sample based on genre, artist, and lyrics as input. Previously Open AI has also released a GPT-2-based MuseNet model which may generate 4-minute musical compositions using 10 instruments.

Although generative audio models are of their nascent phase, the room for increasing the productivity of musicians and sound engineers will only grow every 12 months with higher generative AI music tools.

5. Knowledge Employees: Youtubers & Video Content Creators

Video content is booming. There have been roughly 51 million YouTube channels in 2022. The production of video content goes through several stages, including recording, editing, adding illustrations and sounds, and pre and post-production.

Generative AI video platforms are easing video content generation for knowledge staff. Tools like Synthesia.io, and Pictory, are making video generation easier for video marketers and branding experts. These state-of-the-art AI platforms allow content creators to make videos from scripts. They will add a narrator and a video background to make professional-looking videos based on these scripts.

In September 2022, Meta AI released Make-A-Video platform that may generate high-quality video clips based on text prompts. It was trained on publicly available datasets to learn video patterns. It could create unique videos which might be filled with colours, characters, and landscapes.

Creating more quality content briefly time spans will enhance the productivity of YouTubers and video content creators in the long run.

Pros and Cons of Generative AI for Knowledge Employees

Let’s take a look at the assorted advantages and disadvantages that generative AI presents to knowledge staff.

Pros of Generative AI for Knowledge Employees

1. Synthetic Data Generation: Training modern AI models require ample amounts of datasets and generative AI can solve this problem. Reportedly, generative AI will account for 10% of all data produced in 2025 as in comparison with 1% in 2023. Hence, data scientists and AI experts won’t need to face the challenges related to data collection. 
2. Low Costs: Gartner predicts that around 50% of low-code/no-code development platforms will provide “text to code” functionality by 2024. For developers, this implies more features with the least effort and value. 

Cons of Generative AI for Knowledge Employees

1. Synthetic Content Detection: Although generative AI increases productivity, the issue to detect generative AI content and distinguish it could turn out to be a serious concern in research and academia. By 2024, the European Union will pass laws to mandate the “watermarking” of AI-generated artifacts.
2. Unemployment: Developers can face unemployment if generative AI becomes “too” intelligent. Gartner predicts that by 2025, 20% of procedural code professionals would have to realize recent skills because generative AI will take over their core skill set. 

The Cost of Constructing Generative AI Models

Generative AI is by far probably the most modern branch of AI. Currently, the associated fee of coaching a generative AI model is high, but progressively declining. For example, the estimated cost of coaching GPT-3 was $4.6 million in 2020. In 2022, it has come right down to $450,000.

Cost to coach GPT-3. Source: ARK’s Big Ideas 2023

The ARK’s Big Ideas 2023 report predicts that by 2030 AI models with 57 times more parameters than GPT-3 (175 B parameters) might be trained for under $600,000. This shall be largely possible on account of decreasing costs for training AI models. Wright’s Law suggests that AI relative compute unit (RCU) production costs and software costs should decline by 57% and 47% at annual rates, thereby, leading to a 70% drop in training costs annually till 2030. 

AI training hardware cost. Source: ARK’s Big Ideas 2023.

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