Tipping point in AI adoption
Generative artificial intelligence (AI) is at the tipping point of mass consumer adoption following the successful launch of ChatGPT. Since its public release at the end of November 2022, ChatGPT has become the fastest-growing consumer app in history, reaching over 100 million active monthly users in January 2023. The previous record was held by TikTok, which broke the 100 million-user threshold in 9 months.
Generative AI may impact the real economy much sooner than most people expect. Unlike previous emerging technologies, such as blockchain and the metaverse, generative AI has wide-ranging real-world applications and is easy to use without additional hardware requirements.
With the potential to redefine existing business models, companies are racing to embed the technology in their products and services. First-movers that deliver a high-quality user experience may be able to gain substantial market share, while slow-moving incumbents risk disruption.
Only the beginning
Generative AI is a broad label describing algorithms used to create new original content including text, images, audio, and videos. Using pre-trained models and advanced computational hardware, these algorithms can produce content that is almost indistinguishable from that produced by humans.
Current applications focus on augmenting creative work. Text-based applications like ChatGPT can write essays, debug computer programs, create original jokes, and solve mathematical problems. Image-based applications can create graphics and artwork from textual inputs.
Future commercial uses for generative AI may be far more sophisticated. It has the potential to “invent” new designs of drugs, materials, and semiconductor chips by analysing and building on aspects of large volumes of data that humans may have missed.
Ultimately, generative AI should bring about greater productivity; the technology may help to automate processes and reduce the need for human labour. Just as robotics automated manual work in factories, generative AI could have a profound impact on the legal, marketing, banking, advertising, and media industries.
Drawbacks and risks
Being in the early stages of development, it may take time for financial benefits to materialise for companies seeking to monetise generative AI. Most products have yet to be rolled out commercially, and the race to launch these applications may require significant investments in R&D as well as in computing hardware.
The “black box” nature of generative AI algorithms – where models are trained on large volumes of data to produce new content – can lead to legal and copyright issues. These AI models do not generally attribute sources to their content and can sometimes provide factually incorrect or biased answers.
With the potential to become human-competitive at general tasks, the technology will inevitably face scrutiny and regulation. Issues can become politicised and companies that have poured resources into the development of generative AI tools risk writing down their investments.
Among the three main categories of tech companies that may benefit from the adoption of generative AI, semiconductor companies could be the first to see a positive financial impact, while enterprise software and cloud computing infrastructure companies may benefit over time.
The need for advanced computational hardware to drive AI models may provide structural demand for semiconductors. Leaders in chip design, networking, logic and memory chip manufacturing and semiconductor production equipment stand to benefit.
Software companies that have clear technology leadership, access to data hosted in the cloud and that are close to commercialisation of their generative AI products can also reap early returns. Examples of these are search engines, as well as content creation and design software. Large platforms with cloud-based applications will have a competitive advantage since they have access to huge volumes of data to train and improve their AI algorithms. They should be able to gain market share and sell more premium packages including generative AI features.
As generative AI applications become more pervasive across industries, the adoption and use of cloud computing should also increase. Cloud computing service providers can benefit from this by developing and hosting AI tools for small companies which will not have the resources to run their own generative AI models.