The rise of consumer AI

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Artificial intelligence (AI) is a technology that has been around for a while, dating all the way back to the 1950s, according to many sources. This was just around the time that Alan Turing published “Computer Machinery and Intelligence,” which famously posed the question, “Can machines think?”

Subsequently, American computer scientist John McCarthy coined the term artificial intelligence, in his proposal for the 1956 Dartmouth Conference, the first major conference devoted to the subject. In the proposal, McCarthy, along with other prominent researchers like Marvin Minsky, Nathaniel Rochester, and Claude Shannon, described their research interest as “the conjecture that every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it.”

As you can see, AI has been around for multiple decades. Still, it has only been recently that AI-powered technologies have been mass-adopted. This is because of consumers, particularly non-technical users, which has caused this historically niche technology to have a network effect that you don’t see exclusively in enterprise settings.

So, what catalyzed this transition? How did AI evolve from a niche academic pursuit to a ubiquitous element in our daily lives?

Making advanced technology accessible to all

It’s crucial to recognize that the surge in AI interest is not about the inception of the technology, which has been around for quite some time, but rather its rapid spread in recent years. In my opinion, there are two significant factors behind this rapid expansion. Firstly, the culmination of over 50 years of advancements in technology.

Decade	Year	Event
1950s	1950	Alan Turing proposes the Turing Test as a measure of machine intelligence.
1950s	1956	The term ‘Artificial Intelligence’ is coined at the Dartmouth Conference.
1960s	1961	The first industrial robot, Unimate, starts working on assembly lines.
1960s	1965	Joseph Weizenbaum creates ELIZA, an early natural language processing computer program.
1970s	1972	The first AI home assistant, the Kitchen Computer, is advertised but never sells.
1970s	Late 1970s	AI winter begins, leading to reduced funding and interest in AI research.
1980s	1980	Digital Equipment Corporations develops R1 (also known as XCON), the first successful commercial expert system.
1980s	1987-1993	The second AI winter occurs, due to the collapse of the Lisp machine market.
1990s	1997	IBM’s Deep Blue defeats world chess champion Garry Kasparov.
1990s	Late 1990s	Machine learning emerges as a major strategy for AI development.
2000s	2006	The term ‘Deep Learning’ is introduced to the AI community.
2000s	2011	IBM’s Watson wins Jeopardy!, a major milestone in natural language processing.
2010s	2014	Google acquires DeepMind; later, DeepMind’s AlphaGo defeats world Go champion.
2010s	2018	AI ethics become a major topic of discussion, addressing issues like bias in AI.
2020s	2021	GPT-3, one of the most sophisticated generative language models, is widely recognized for its capabilities.
2020s	2023	AI continues to integrate into various sectors, raising questions about the future of work, ethics, and governance.

 

This sustained research and development within the field of AI have been instrumental in bringing the technology to its current state. Without this groundwork, AI wouldn’t be as accessible and functional for non-technical individuals as it is today. These technological advancements have enhanced the capabilities of AI systems and broadened their applicability, allowing consumers and businesses to utilize AI in more advanced ways. It’s the decades of innovation and progress in AI that have culminated in this point in history where laypeople can use AI in their daily lives and workflows.

The second factor driving the recent acceleration of AI into the consumer sphere is that AI has become much more user-friendly, especially for those who aren’t technical. While the concept of AI was broadly known, direct interaction with it was limited for most people. Most people were aware of AI’s role in powering search algorithms and social media recommendations, but these were predominantly outputs controlled by businesses, not tools that the users could actively engage with or choose to utilize in their daily lives.

However, this dramatically changed with the release of ChatGPT in November 2022. When ChatGPT launched, for perhaps the first time in history, consumers were given the opportunity to interact with an AI system on their own terms, as long as their activities were text-based. With a vast database encompassing books, movies, texts, and an array of other information from around the world, ChatGPT provides endless possibilities for its users with its ability to quickly access, analyze, and make sense of large datasets. This versatility meant that users could tap into a wide range of AI-induced use cases.

The platform quickly amassed over 150 million users and marked a significant moment in history where, for the first time, non-technical consumers could incorporate AI into their everyday lives.

Thanks to Large Language Models (LLMs) and their ability to rapidly access, analyze, and interpret large datasets, consumers use AI as their assistants, consultants, project managers, personal recommendation engines, and more. This ranges from tapping into AI to provide expert advice and devising optimal solutions within given constraints to simply offering quick answers to user queries as a better substitute than a traditional search engine. We are also seeing AI enter the workplace, with employees showing optimism towards AI as a tool that automates tasks or acts as an assistant or aid but remaining cautious, or even fearful, about AI possibly replacing human roles in the workplace.

Why consumer AI went viral

There is undoubtedly a significant amount of value in the “AI for business” vertical. Among many other use cases, businesses can take their large datasets, have AI systems identify patterns, and make predictions that improve their operations and increase their bottom line. However, products and services for enterprises do not tend to have the same network effect as consumer products.

Because of word-of-mouth and social sharing, consumer products often benefit from faster viral spread. The value of consumer products frequently increases as more people in one’s personal network use them, enhancing personal interactions and shared experiences—we are likely to see this element of the network effect unfold when the GPT store is launched later this year.

Additionally, consumer products usually target a broad and diverse user base, which can sometimes lead to a more robust network effect versus enterprise products, which tend to be industry-specific and slower to be adopted because the cost to switch to the new technology can be high for a business for multiple reasons, for instance, the business might not have the personnel to implement or operate the new technology.

That being said, ever since chatGPT hit the market, it has really been consumers, and not businesses, that we can credit for being the driving force behind the rapid spread of AI. The consumers of the world are the reason that OpenAI and ChatGPT are now household names and the reason why AI and its potentially positive and negative impact on the world are part of dinner table conversations.

Businesses capitalizing on consumer tech trends

Although B2B SaaS is valuable but not exactly able to experience the social phenomenon of a network effect like consumer-facing products, businesses are waking up and offering consumer products that coincide with the consumer AI wave.

On the hardware front, manufacturers recognize the potential of consumer AI, leading to AI-powered devices that aim to enhance how consumers interact with their environment, such as what the Meta (NASDAQ: META) Rayban Smartglasses and the Humane Ai Pin are looking to accomplish.

When it comes to consumer electronics, companies are increasingly branding their products as “AI Devices.” This trend is evident in Microsoft’s (NASDAQ: MSFT) announcement of AI PCs set to enter the market and LG’s (NASDAQ: LPL) recent unveiling of an AI TV.

Both developments highlight a shift towards offering AI-powered devices that promise enhanced user experiences, signaling a new era in consumer technology where AI is not just a feature but a fundamental aspect–and potential selling point–of the user experience.

How consumer-driven AI is transforming technology

Thanks to the advancements and innovations in the field, AI has made the journey from being a niche technical field to a consumer-driven phenomenon. Although businesses and technologists have played a significant role in getting AI to where it is today, it is the consumers, to be even more specific, the ChatGPT users, who have truly catalyzed AI’s rise to popularity. The widespread appeal of AI can be attributed to its wide variety of use cases, user-friendly interfaces, and the efficiency it brings to our daily lives—this is the real reason that AI has become a constant topic of discussion for the past two years.

The transformative moment for AI came when AI platforms became accessible to non-technical individuals. This democratization of AI technology spurred a wave of innovation.

Previously, many AI products and services were designed by and for the technically adept, but with broader accessibility, we are witnessing the emergence of AI applications usable by a significant portion of the population, which only strengthens the network effect we see taking place. This shift has led to people sharing their unique AI usage stories and creating value in various aspects of their lives and work, whether by adopting others’ prompts or exploring new ways to integrate AI into their daily routines. But, where there are consumers, there will be businesses looking to make a buck. Electronics manufacturers have responded by developing products that complement and enhance how consumers are interested in using AI.

Both trends are set to grow as the underlying technology continues to evolve, and insights into consumer demand for AI applications become clearer. The industry is increasingly focused on creating products and services that meet these changing consumer needs, ensuring that AI remains not just a technological fad but a practical and integral part of our everyday lives.

In order for artificial intelligence (AI) to work right within the law and thrive in the face of growing challenges, it needs to integrate an enterprise blockchain system that ensures data input quality and ownership—allowing it to keep data safe while also guaranteeing the immutability of data. Check out CoinGeek’s coverage on this emerging tech to learn more why Enterprise blockchain will be the backbone of AI.

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