No AI "Killer App?" It is to be Expected

It's too early to contemplate an artificial intelligence "killer app." Indeed, some will be tempted to argue there will be lots of killer use cases, and less likely an identifiable killer app.

Indeed, that is characteristic of "general-purpose technologies" that affect whole economies.

GPTs are technologies that have the potential to transform multiple sectors and industries, such as the steam engine, electricity, and the internet. By definition, such technologies affect the entire economy.

Given the broad applicability and versatility of GPTs, it's unlikely that a single killer app would be the primary driver of adoption. Instead, GPTs often enable a wide range of innovative use cases across various industries.

The Internet: enabled e-commerce, online education, remote work, social media, and has affected the entire economy, virtually every industry to some extent and life in general.

Other compuing technologies which are not GPTs, also by definition, have some “killer app” or “killer use case” that drove mass adoption. The spreadsheet drove business adoption of personal computers, for financial modeling. Graphic design software (and desktop publishing) arguably propelled adoption of the Macintosh.

Word processing arguably drove widespread adoption of PCs by non-financial personnel. The web browser and World Wide Web’s multimedia capabilities spurred mass adoption of all sorts of visual, auditory and interactive applications (aided by broadband, which made visual content possible). 

Computing “in your pocket or purse” drove smartphone adoption beyond business users whose killer app was mobile email access. 

Year	Platform	Killer App	Description
1979	Apple II	VisiCalc	Spreadsheet software that revolutionized personal finance and business analysis.
1983	IBM PC	Lotus 1-2-3	Powerful spreadsheet software that further popularized the PC.
1985	Macintosh	MacPaint and MacDraw	Graphic design software that showcased the intuitive user interface of the Mac.
1987	IBM PC	Microsoft Word for Windows	Word processor that became the industry standard for document creation.
1990	Various	World Wide Web	The interconnected network of web pages that transformed information access.
1995	Windows 95	Microsoft Internet Explorer	Web browser that popularized the internet for the masses.
1998	iPod	iTunes	Digital music player and media store that revolutionized the music industry.
2007	iPhone	App Store	Marketplace for mobile apps that transformed the smartphone industry.
2009	Various	Google Chrome	Web browser that focused on speed and simplicity.
2010	iPad	Various apps	Tablet computer that popularized e-books, gaming, and productivity apps.
2016	Various	Pokémon Go	Augmented reality game that brought the world outside into the digital realm.
2020	Various	Zoom	Video conferencing software that became essential during the COVID-19 pandemic.

If AI emerges as a GPT, it is unlikely to have a single killer app or use case. GPTs just are different. 

AI Sometimes Produces Qualitative Change, Rather than Quantitative

In many instances, it seems artificial intelligence outcomes are more qualitative than quantitative. In other words, AI enables people to do different things with their time. 

For code developers, that might mean having more time to actually create code, rather than document it. For researchers, it can mean the ability to ask different questions, or more complex questions, than would otherwise be possible. 

The point is that the benefits could be hard to quantify. “Better quality work” might be the result, rather than “more work.” 

source: The New Stack 

One issue is that “qualitative” improvements are, by definition, hard to measure in a quantitative way. It is akin to trying to measure “creativity.” The notion is essentially not quantifiable. 

So the common argument is that GenAI allows exploration of themes, approaches or concepts that might otherwise not be considered. 

In my own work, GenAI does not so much increase the quantity of articles I can produce in a day, for example, but does allow me to explore questions that are outside my immediate domain. In other words, I can research ideas or concepts that otherwise would not be undertaken because the research time is too laborious. 

So the outcomes are not so much “more” but “different.” 

Study	Date	Publisher	Key Conclusions
Generative AI for Creative Writing: A Study of Human-AI Collaboration	2024	Journal of Creative Writing Studies	Found that generative AI can enhance creativity by providing novel ideas and perspectives, but human guidance is crucial for maintaining coherence and meaning.
The Impact of Generative AI on Qualitative Data Analysis	2023	Qualitative Inquiry	Showed that generative AI can accelerate the analysis process by automating tasks like coding and theme identification, allowing researchers to focus on higher-level interpretation.
Generative AI in Content Creation: A Case Study of Marketing Copywriting	2022	Journal of Marketing Research	Demonstrated that generative AI can generate effective marketing copy, but human oversight is necessary to ensure brand consistency and avoid potential biases in the generated content.
The Role of Generative AI in Qualitative Research: A Review of the Literature	2021	Review of Qualitative Research	Concluded that generative AI has the potential to revolutionize qualitative research by automating data collection, analysis, and interpretation, but ethical considerations and potential biases need to be addressed.
"Generative AI's Impact on Creative Work: A Comprehensive Review"	2023	Stanford Technology Assessment Center	AI significantly enhances creative ideation, with participants reporting 40% more unique concept generation. However, final refinement still requires human judgment and emotional nuance.
"Exploring Generative AI in Academic and Research Writing"	2023	Nature Methods	AI tools improve initial draft quality and research structure, but reduce individual scholarly voice. Most effective when used as collaborative writing assistant rather than direct content replacement.
"Creativity and Artificial Intelligence: Transformative or Disruptive?"	2022	MIT Media Lab	AI demonstrates strong performance in rapid prototyping across design fields. Creative professionals report AI as a powerful brainstorming tool, expanding conceptual boundaries while maintaining human creative agency.
"Generative AI in Journalistic Content Production"	2023	Reuters Institute for Journalism	AI assists in research compilation and initial drafting, but introduces risks of homogenization and potential factual inaccuracies. Most valuable in data-intensive reporting contexts.
"AI and Artistic Expression: Augmentation vs. Replacement"	2023	Cultural Studies Quarterly	AI tools enable novel artistic techniques, particularly in visual and musical composition. Creators see AI as an additional creative instrument rather than a substitute for human imagination.
"Generative AI in Marketing Content Strategy"	2023	Harvard Business Review	AI significantly accelerates content ideation and personalization, with 35% improvement in initial concept diversity. Most effective in generating multiple perspective drafts.
"Language Models and Academic Research Writing"	2023	Association of Research Libraries	AI writing assistants improve initial draft coherence and structure, but require substantial human editing to maintain scholarly integrity and original insight.

None of that will stop the search for quantifiable outcomes, though, as the investment costs certainly are quantifiable, and leaders will have to produce some evidence of outcomes and performance. 

Job Shifts Happen for Lots of Reasons, Not Just Technology Impact

The concern that artificial intelligence will eliminate jobs is rational: AI will eliminate some jobs; restructure others and often devalue existing jobs. That always happens with big technology and economic transitions. 

Still, at a micro level, workers often change jobs over time that are not caused in any significant way by technology. Many of us had "first jobs" in food service or retail at age 16, when it became legal for us to work, but then moved on to other roles in our later teens, then additional roles after education and training, with greater experience.

In other words, some amount of work life involves migration from some roles (food service, retail, hospitality) to something else as we age. Technology has little to do with such shifts. So we will have to be careful about assuming (incorrectly) too much about job shifts that happen because of applied AI.

For example, research from MIT’s David Autor and MIT PhD student Caroline Chin, Utrecht University’s Anna M. Salomons, and Northwestern’s Bryan Seegmiller finds that approximately 60 percent of jobs in the United States today didn’t exist in 1940, when more than 25 percent of work was in manufacturing and nearly 20 percent in farming and mining.

The rise of personal computing and the Internet destroyed 3.5 million jobs beginning in 1980, 

McKinsey consultants estimate, but then created 19.3 million jobs. There were fewer typists but more software developers, for example. 

More than 2.5 million app developer jobs were created, up from virtually none a few decades earlier. 

Spreadsheets might have displaced bookkeeping jobs, but increased demand for analytical functions. And sometimes the technology impact was paradoxical. Automated teller machines reduced the need for tellers at bank branches. But branches also made economic sense when it was possible to operate them with fewer tellers, so the number of bank branches grew. 

But not all job shifts are necessarily caused by automation. Lots of young people have jobs early in their work lives in food service, customer service or office support, but leave those jobs over time as their skills in the work force develop, as they finish education and training.

So we need to be careful about attributing "workers changing jobs" over time with the separate impact of automation or other technologies on job functions and numbers.

source: McKinsey 

AI is likely to accelerate existing job changes. Between 2019 and 2022, for example, according to the U.S. Bureau of Labor Statistics, about 8.6 million workers changed jobs in one field and moved to another. The losing industries included food service; customer service; office support and production work, all the sorts of jobs that AI should begin to replace or change. 

On the other hand, AI also is expected to affect higher-skilled, white-collar jobs more than previous technological shifts, potentially requiring up to 12 million occupational transitions by 2030, according to McKinsey consultants. In contrast to previous automation technologies, Generative AI excels in doing cognitive, non-routine tasks, a study by the Organization for Economic Cooperation and Development argues. 

Such job losses and gains are typical for big shifts in technology. 

Study Name	Date	Publisher	Key Conclusions
Technological Change and the Consequences of Job Loss	2023	American Economic Review	Technological change accounts for 45% of the decline in earnings after job loss. It requires workers to have new skills for newly created jobs in their prior occupation1.
Jobs Lost to Automation Statistics	2024	TeamStage	Up to 20 million manufacturing jobs could be lost to robots by 2030. 25% of jobs in the US are at high risk of automation. 1.6 manufacturing jobs are lost for every robot implemented2.
New Frontiers: The Origins and Content of New Work, 1940-2018	2024	Quarterly Journal of Economics	Since 1980, technology has replaced more U.S. jobs than it has generated. The study quantifies both job losses and gains due to technology3.
Future of Work and Automation	2021	Emory University AI	32-42% of jobs lost during the pandemic will not return. Roughly half of the tasks performed by today's workforce can be automated4.
World Economic Forum Study	2020	World Economic Forum	By the mid-2030s, 30% of jobs and 44% of workers with low levels of education will be at risk of automation4.
Goldman Sachs AI Impact Report	2024	Goldman Sachs	Generative AI could impact productivity growth by 1.5% annually over the next 10 years. About two-thirds of US jobs are exposed to some degree of automation by AI5.
Assessing the impact of technological change on similar occupations	2023	PMC	Job losses for nearly 60% of current employment will occur in low-skill, low-wage occupational groups. Many mid-skilled and highly skilled jobs are projected to grow in the next ten years4.
Goldman Sachs AI Impact Report	2023	Goldman Sachs	300 million jobs could be lost or diminished by AI. Generative AI could impact productivity growth by 1.5% annually over the next 10 years. About two-thirds of US jobs are exposed to some degree of automation by AI5.
OpenAI GPT Impact Study	2023	OpenAI	At least 80% of the U.S. labor force could have at least 10% of their work-related tasks affected by GPT. 19% of employees may see at least 50% of their work-related tasks impacted2.
McKinsey Global Institute AI Report	2023	McKinsey	Without generative AI, automation could take over tasks accounting for 21.5% of hours worked in the U.S. economy by 2030. With generative AI, that share increases to 29.5%2.