ChatGPT and beyond: Why generative AI’s inflection point might be Tech’s most promising new market opportunity
ChatGPT represents an important inflection point for AI, and an important one for the tech industry at a time when consumers, worn down by perhaps one too many hype cycles, have begun to lose faith in the sector’s ability to deliver on its promises.
The tech industry’s current headwinds aren’t limited to inflationary pressures, a global economic cooldown, supply chain issues, and COVID resurgences. There is one other key factor at play: the impact that consumer perceptions of new and emerging tech regarding trust, value/utility, and excitement have on demand. The feedback I have consistently received from the consumer trenches over the last three years suggests that years of tech hype, endless streams of vaporware and grandiose but empty promises have made consumers both confused by and suspicious of new and emerging technologies that either keep being delayed or that systematically disappoint. I refer to this phenomenon as hype fatigue, and tech companies need to start taking the role this plays into their marketing, PR, and product development more seriously. Hype fatigue doesn’t just lower consumer interest (demand) in new tech, it also tends to make them distrustful of the messengers that keep carrying hollow tech hype to them on a regular basis. The below are examples of too much hype and not enough substance.
- The colossal clown shows that the crypto and NFT spaces devolved into last year certainly didn’t help, but the problem runs much deeper: Privacy and data security still struggle to inspire confidence.
- Mark Zuckerberg’s head-scratching vision for “metaverse” is drawing more side-eyes than traction, and is at best a decade away.
- Bringing augmented reality glasses to consumers has been a predictable annual exercise in kicking the proverbial can down the road.
- The promise of fully autonomous vehicles being just a year away has been stalled by crashes and terrifying failures.
- The evolution of the smart home fizzles out as soon as the excitement of CES is over.
- Artificial intelligence has, for the most part, proven itself to be more artificial than intelligent; VR and XR, for all their promising and exciting use cases, have failed to justify investments at scale outside of gaming.
In short, the biggest and most exciting technology plays consumers keep being told are right around the corner has, for the most part, either failed to launch or haven’t exactly impressed. “Wake me up when [insert technology here] is actually ready” seems to be the attitude in most households, and that isn’t the consumer perception trajectory that will help the tech sector bounce back from what is shaping up to be a difficult 2023. “Where’s the beef?” the old ad went.
“Where’s the beef,” indeed.
What the tech sector needs to disrupt hype fatigue isn’t exactly rocket science. (Well… it might actually be rocket science, but I digress.) Conceptually, it’s a simple fix: less hype, more beef. That’s it. Replace overpromising with overdelivering. More specifically, on the UX and product development front, new tech solutions can’t just be okay or pretty cool, but legitimately remarkable. And by that, I mean surprisingly good, and if possible, shockingly better than expected. New tech should dazzle, enchant, bring magic and excitement back into people’s lives, not just on paper or in promises, but when they actually get their hands on it. Short of that, new tech needs to actually be useful. I don’t mean as cool solution in search of a problem (a hurdle that VR still struggles to overcome) but as something that creates immediate, undeniable value for users. Ideally, new tech does both, but one or the other will do for now. 2022 and 2023 were low bar years, after all. And that is where generative AI enters the chat, and specifically ChatGPT.
Why ChatGPT and generative AI’s unexpected evolutionary leap is a massive market opportunity for the tech sector
Despite a flurry of exciting AI-related announcements from tech giants like Intel, Amazon, Google, Microsoft, Apple and Qualcomm last year, along with some encouraging implementation tailwinds, consumer perceptions of AI remained mixed, with the technology continuing to feel confusing, at times frustrating, even untrustworthy. That all changed when people started playing with ChatGPT and seeing for themselves just how uncannily good it was not only at understanding queries but at generating replies and content.
To be fair, it wasn’t the first time that an AI product has delivered for consumers. As limited as they still are, Google Assistant and Alexa have been remarkable advancements (more so than Siri, in my view) in the voice interface/virtual assistant space, for example. Less obvious to consumers but equally valuable are AI solutions that optimize processes at scale, from ad targeting, pattern recognition, and data analysis to ADAS systems and computational photography. But ChatGPT is different in that it seemingly came out of nowhere and unsaddled by hype or expectations, managed to blow everyone away by being so surprisingly powerful and fun to use. It is so surprisingly good, in fact, that it immediately raised a flurry of questions about the potentially devastating impact it might have on job security, academia, and creative industries. The degree to which ChatGPT instantly felt disruptive and fun but dangerous speaks to how much of an inflection point it created for AI, and generative AI in particular.
Educators immediately realized the danger this posed to educational models that rely on research papers to evaluate students: If ChatGPT could write papers as well as a human, many students would naturally lean on the technology to save time and improve their grades without necessarily learning the material. Every problem is an opportunity, though, and this one needs to scale quickly: ChatGPT has shown us that the market needs effective AI detection tools capable of the kind of nuanced analysis that can differentiate between AI-authored copy and AI-edited copy. Every school in the world is going to need to fold this into their tech stack now, with appropriate policy frameworks. This could be an instant mass market opportunity for the right software vendors.
What about the threat this tech poses to journalism and copywriting? Will generative AI tools like ChatGPT accelerate layoffs in funding-strapped newsrooms, marketing firms, web design firms, and ad agencies? Can generative AI write code faster and as well as human web developers? The collective gasp from professionals who suddenly realized that their job could be done by an AI is, in its own way, another indicator that ChatGPT and generative AI changed the world virtually overnight. The market opportunity this challenge has created is more granular than it is for academia, but that isn’t a bad thing for solutions vendors. That’s because generative AI tends to be more powerful and economically sound as an optimizing tool for human workers than as an outright replacement of human workers. Unless an organization’s model is to produce junk mail, it is going to need talented, qualified, experienced workers. Writers, attorneys, accountants, paralegals, problem-solvers, decision-makers, leaders, and so on. How an organization maximizes the power and potential of generative AI tools is by empowering their staff to incorporate them into their workflows. The true potential of generative AI, at least for the foreseeable future, isn’t job automation, but rather, task automation.
In other words, generative AI solutions should be used to enhance human workers, not replace them outright. Once decision-makers and technology users understand that nuance, the “how does generative AI fit into my organization/role” question becomes much easier to answer. Reframed from that perspective, the question now becomes “how can generative AI tools help me work faster and better instead of just working harder?” Also: “How can generative AI save me time? How can generative AI allow me to focus on more valuable tasks? How can generative AI give me more time to do other things?” The value equation shifts.
Examples of how generative AI tools can help specialized staff become more effective:
- An executive can use a generative AI tool to summarize news items or bring clarity to a complex topic. It can also be used to quickly generate drafts for up-to-date reports and deck content for meetings.
- A copywriter, journalist or researcher can turn to a text-based generative AI tool to shorten a piece to fit within a specific word count limit, or explore different styles of writing to find the right tone and voice for a piece, or organize their work to conform to a specific style of writing.
- Educators can similarly use a text-based generative AI tool to better organize their lesson plans, ensure that they fit within the right time windows, emphasize key points for students, and generate homework assignments, lesson summaries, and test questions.
- For students, a text-based generative AI can help summarize a lesson or topic, essentially working as a tutor to help them better understand, study, memorize, apply, and retain lessons. When used ethically, it can also help a student organize their notes and thoughts around a topic to develop outlines and drafts for research papers and essays, and speed up proofreading and editing.
- For attorneys and their paralegals, a text-based generative AI can be leveraged to draft contracts and review them faster, proofread or search for keywords in depositions and court transcripts, speeding up these processes and allowing them to take on more clients and/or increase their billable hours.
The opportunities for generative AI solutions are close to limitless, both for the enterprise and for individuals, and landing on the optimal price points for a tiered monthly/annual subscription model won’t be difficult to figure out. The fundamental foundation powering things like ChatGPT and the recent breakthroughs with large language models will likely help with that.
The bigger the inflection point, the bigger the disruption, and the bigger the disruption, the bigger the market opportunity. Since VR, AR, ADAS, EVs, the metaverse, and other potentially transformational technologies may take a while to scale and deliver the value we were promised they ultimately will, 2023 offers the tech sector a unique and welcome lifeline – one it can use to reverse hype fatigue with, create entirely new revenue streams from, and leverage to expand existing market share and/or user base. For investors, the opportunity is equal parts investing in solutions vendors with an obvious head start and market advantage, and backing disruptive startups and challengers with the right focus, talent, and ability to execute.