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Artificial intelligence as a disruptive technology—a systematic literature review.

1. Introduction

2. materials and methods.

• First exclusion: ○ Document types—the Editorial Materials and Meeting Abstracts were removed (WoS—38, S—42), leaving 124 (WoS) + 142 (S) = 266 papers; ○ All publishers with only 1 article, as we considered that they did not have a serious approach toward this topic, were removed (WoS—20, S—23), leaving 104 (WoS) + 119 (S). Further, at this stage, the intermediary results (1) were merged into the same file, resulting in 223 articles.
• Second exclusion: ○ With the support of EndNote (used for reference management), it was possible to identify duplicate records (196) originating from the two databases and retain only 1 entry (98). In this manner, we obtained the intermediary results (2), with a total of 125 references.
• Third exclusion: ○ The remaining list was evaluated for relevance based on title, keyword, and abstract analysis, and the articles that did not fit the purpose of the research were eliminated (−28), leaving a total of 97 papers included in the study.

3.1. AI as a Disruptive Technology in Healthcare (Medicine)

3.1.1. disruptive features in the applications to surgery, 3.1.2. disruptive features in the applications to healthcare, 3.2. ai as a disruptive technology in business—logistics and transportation and the labor market, 3.2.1. logistics, 3.2.2. labor market, 3.3. ai as a disruptive technology in agriculture, 3.3.1. smart farming, 3.3.2. digital twins, 3.3.3. the fourth industrial revolution (4ir), 3.4. ai as a disruptive technology in education, 3.5. ai as a disruptive technology with respect to urban development—society, smart cities, and smart government, 3.5.1. disruptive technology’s impact on society, 3.5.2. smart cities, 3.5.3. smart government, 4. discussion and conclusions.

• Enhanced diagnosis, as AI algorithms can examine a large number of medical data to help clinicians make more accurate diagnoses, thus minimizing the possibility of misdiagnosis;
• Personalized medicine, since by using a patient’s particular medical history and genetic data, AI can aid the development of individualized treatment approaches;
• Superior patient outcomes, as AI may be used to track patients, anticipate future health difficulties, and alert medical professionals to take preventative action before significant health issues arise;
• Expedite drug development, because AI can analyze massive volumes of data to hasten the process of developing new drugs and bringing them to market;
• Improved clinical trials, due to the fact that data from clinical trials may be analyzed using AI algorithms, thus assisting in the selection of the most efficient therapies and enhancing patient results.
• The development of AI in healthcare creates ethical issues, such as the issue of responsibility in situations of misdiagnosis or treatment suggestions;
• Limited clinical validity poses a serious problem, because in certain complicated medical situations, AI algorithms may not be as accurate as human specialists and may not be completely verified for assessing all medical disorders;
• Healthcare professionals and patients who are suspicious about the accuracy and dependability of the technology can be resistant to the adoption of AI in the industry.
• For improved supply chain management, AI may aid routing, scheduling, and delivery optimization, which lowers transportation costs and increases delivery times;
• Transportation safety may be improved by using AI to track and improve driver behavior, reduce collisions, and increase road safety;
• AI can enhance logistics efficiency, as it may be used to improve inventory management, optimize storage and picking procedures, and expedite warehouse operations;
• AI is transforming the labor sector by replacing many old manual jobs while also opening up new career prospects in programming and data analysis;
• AI may improve customer experience as it can be used to offer updates on tracking and delivery in real-time, thereby reducing wait times and raising satisfaction;
• AI may aid the maximization of fuel use and the cutting of emissions through effective vehicle scheduling and routing and thus contribute to minimized environmental impacts;
• Many laborious and repetitive tasks will be automated, which may result in fewer jobs and employment possibilities, particularly in sectors such as logistics and transportation;
• As the demand for more high-skilled positions in AI and data analysis increases and fewer low-skilled occupations are automated, the rising usage of AI may worsen already-existing income discrepancies;
• The widespread usage of autonomous cars may result in substantial social and cultural changes, such as the loss of individual driving abilities and the demise of the automobile culture.
• Improved agricultural yields and less waste are possible with the use of AI, which may help farmers optimize planting, irrigation, and fertilization;
• Better resource management may help farmers conserve energy, water, and other resources while decreasing waste and enhancing sustainability;
• Enhanced food safety can be enforced by tracking the whole food production chain from farm to table, while AI can assist in the identification and prevention of food-borne diseases;
• AI can provide real-time analysis of crop, soil, and weather variables, thus enabling farmers to make educated decisions;
• Predictive maintenance may reduce downtime and boost production by predicting when machines and equipment need maintenance.
• AI systems are not immune to technical glitches or malfunctions, and the agricultural sector might suffer significantly as a result, leading to crop losses and possible food shortages;
• The usage of AI in agriculture may have unforeseen environmental effects, including increased pesticide and herbicide use, degraded soil, and the loss of biodiversity.
• A decrease in dropout rates and improved student results due to AI’s ability to detect students’ areas of need and offer focused support;
• Education that is customized to each student’s requirements, interests, and learning preferences may be achieved by using AI to deliver personalized learning experiences for students;
• Improved assessment and feedback due to AI’s ability to automate, enhance, and optimize the grading and feedback process and provide students faster, more precise, and more thorough feedback on their work;
• Lifelong learning is possible because of AI, which can help people continue to learn and advance their expertise.
• Education quality may suffer due to the usage of AI in the classroom when human interaction, creativity, and critical thinking abilities are substituted by automated procedures;
• A lack of critical thinking abilities may be precipitated by AI because the use of AI-powered tools and resources may lessen the necessity for critical thinking and problem-solving abilities, which may retard the development of these skills among students;
• The dependence on technology due to an overreliance on AI in the classroom may result in a lack of creativity, independence, and decision-making abilities, which will reduce students’ capacity to think and work independently.
• An increase in transparency, as by using AI to render governmental processes more open and accountable, individuals will be able to better understand how choices are made;
• Enhanced fraud detection, since AI may be used to identify and stop corruption and fraud in government systems, thus increasing public confidence in these organizations;
• Better resource allocation, because governmental organizations may use AI to more effectively direct resources, including money and staff, to the areas where they are most needed;
• The introduction of predictive analytics, as through the use of AI, government agencies may employ predictive analytics to proactively address prospective concerns before they become problems.
• Privacy issues—Government entities frequently deploy AI algorithms that rely on substantial volumes of personal data, which raises privacy concerns regarding how these data are gathered, kept, and used;
• Lack of transparency—AI technologies employed by government agencies may be opaque, making it difficult for the public to comprehend how and why choices are being made;
• The employment of AI in governmental affairs may result in greater control and surveillance, which may have detrimental effects on free expression and civil rights;
• When an AI system utilized by a government errs or causes harm, it may be challenging to pinpoint the culprit, which results in a lack of accountability.

Author Contributions

Data availability statement, conflicts of interest.

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Păvăloaia, V.-D.; Necula, S.-C. Artificial Intelligence as a Disruptive Technology—A Systematic Literature Review. Electronics 2023 , 12 , 1102. https://doi.org/10.3390/electronics12051102

Păvăloaia V-D, Necula S-C. Artificial Intelligence as a Disruptive Technology—A Systematic Literature Review. Electronics . 2023; 12(5):1102. https://doi.org/10.3390/electronics12051102

Păvăloaia, Vasile-Daniel, and Sabina-Cristiana Necula. 2023. "Artificial Intelligence as a Disruptive Technology—A Systematic Literature Review" Electronics 12, no. 5: 1102. https://doi.org/10.3390/electronics12051102

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Disruptive technologies: an expanded view.

• JAMES M. UTTERBACK  and 
• HAPPY J. ACEE

M.I.T. Sloan School of Management and M.I.T. School of Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA

Search for more papers by this author

Delphi Harrison Thermal Systems, Rockport, NY, USA

The term "disruptive technology" as coined by Christensen (1997, The Innovator's Dilemma; How New Technologies Cause Great Firms to Fail . Harvard Business School Press) refers to a new technology having lower cost and performance measured by traditional criteria, but having higher ancillary performance. Christensen finds that disruptive technologies may enter and expand emerging market niches, improving with time and ultimately attacking established products in their traditional markets. This conception, while useful, is also limiting in several important ways.

By emphasising only "attack from below" Christensen ignores other discontinuous patterns of change, which may be of equal or greater importance (Utterback, 1994, Mastering the Dynamics of Innovation . Harvard Business School Press; Acee, 2001, SM Thesis, Massachusetts Institute of Technology). Further, the true importance of disruptive technology, even in Christensen's conception of it is not that it may displace established products. Rather, it is a powerful means for enlarging and broadening markets and providing new functionality.

In Christensen's theory of disruptive technology, the establishment of a new market segment acts to channel the new product to the leading edge of the market or the early adopters. Once the innovation reaches the early to late majority of users it begins to compete with the established product in its traditional market. Here we present an alternative scenario in which a higher performing and higher priced innovation is introduced into the most demanding established market segments and later moves towards the mass market.

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• The Trifurcation of the Labor Markets in the Networked, Knowledge-Driven, Global Economy Meir Russ 27 December 2016 | Journal of the Knowledge Economy, Vol. 8, No. 2
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• Embedding enterprise science into SSM for improving innovation systems in technology‐based companies Karin Löffler, Hugo Tschirky and Kyoichi J. Kijima 9 April 2009 | Systems Research and Behavioral Science, Vol. 26, No. 6
• Option chain and change management: A structural equation application Thierry Burger-Helmchen 1 Jun 2009 | European Management Journal, Vol. 27, No. 3
• A reflective review of disruptive innovation theory Yu Dan and Hang Chang Chieh 1 Jul 2008
• LUTZ PREUSS
• A bridge over troubled waters: Bridging organisations and entrepreneurial opportunities in emerging sectors Jonathan Sapsed, Andrew Grantham and Robert DeFillippi 1 Nov 2007 | Research Policy, Vol. 36, No. 9
• TIM MINSHALL , 
• STUART SELDON , and 
• DAVID PROBERT
• JIM ATTRIDGE
• An emerging market in fuel cells? Residential combined heat and power in four countries James E. Brown, Chris N. Hendry and Paul Harborne 1 Apr 2007 | Energy Policy, Vol. 35, No. 4
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• Mobile Health Technology in the US Tridib Bandyopadhyay and Bahman Zadeh

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Received 2 April 2004 Revised 12 September 2004 Accepted 8 January 2005

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The Ethics of Disruptive Technologies: Towards a General Framework

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• Jeroen Hopster   ORCID: orcid.org/0000-0001-9239-3048 17  

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1410))

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Disruptive technologies can be conceptualized in different ways. Depending on how they are conceptualized, different ethical issues come into play. This article contributes to a general framework to navigate the ethics of disruptive technologies. It proposes three basic distinctions to be included in such a framework. First, emerging technologies may instigate localized “first-order” disruptions, or systemic “second-order” disruptions. The ethical significance of these disruptions differs: first-order disruptions tend to be of modest ethical significance, whereas second-order disruptions are highly significant. Secondly, technologies may be classified as disruptive based on their technological features or based on their societal impact. Depending on which of these classifications one adopts and takes as the starting point of ethical inquiry, different ethical questions are foregrounded. Thirdly, the ethics of disruptive technology raises concerns at four different levels of technology assessment: the technology level, the artifact level, the application level, and the society level. The respective suitability of approaches in technology ethics to address concerns about disruptive technologies co-varies with the respective level of analysis. The article clarifies these distinctions, thereby laying some of the groundwork for an ethical framework tailored for assessing disruptive technologies.

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Hopster, J. (2022). The Ethics of Disruptive Technologies: Towards a General Framework. In: de Paz Santana, J.F., de la Iglesia, D.H., López Rivero, A.J. (eds) New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence. DiTTEt 2021. Advances in Intelligent Systems and Computing, vol 1410. Springer, Cham. https://doi.org/10.1007/978-3-030-87687-6_14

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Phd on how ai technologies could be used to improve ethics methodology, job description.

Can and should philosophers employ large language models or other artificial intelligence tools in the course of doing ethics? Eindhoven University of Technology (TU/e), in collaboration with the inter-university research consortium, "Ethics of Socially Disruptive Technologies (ESDiT)," seeks to hire a PhD student for a four-year project on whether (and if so, how) philosophers can use AI technologies to improve ethics methodology.

Job Description

In the last few decades, philosophers have speculated about how AI systems could support or enhance individual moral reasoning, deliberation, and other moral functioning. Following recent developments in AI research, philosophers may well ask what role AI can play within ethics as a discipline. Can AI technologies be used to improve the methods of ethics, including the methods that ethicists of technology use for responding to socially disruptive technologies?

Socially disruptive technologies pose new types of ethical challenges. AI could conceivably help us respond to those challenges by facilitating better ethical theorizing and decision-making. For instance, philosophers might attempt to incorporate AI technologies into processes of conceptual analysis, conceptual engineering, reflective equilibrium, development of new ethical principles and theories, generation of possible arguments and objections, generation of counterexamples to theories, preliminary identification of morally significant risks and benefits in scenarios, and so on. One way to approach such an ambition would be by using finetuning and special prompting to create large language model-based systems to perform or assist with some of these tasks. Philosophers might also try using AI systems to support exploratory anticipation and prospection activities—developing AI systems that help generate technomoral scenarios with a range of salient features, for human discussion and reflection. To facilitate group deliberation, philosophers might incorporate AI into systems for more frequently and efficiently eliciting, analyzing, and aggregating beliefs and preferences within groups, identifying and characterizing points of overlap and disagreement, and helping humans communicate with other humans about what their values are, what norms they endorse, their reasons for their moral views, etc. Insofar as decisions about technology design, implementation, maintenance, modification, etc., should be informed by the values and preferences of stakeholders, researchers might use AI systems to more effectively request and synthesize inputs from ordinary people about how various designs are falling short in ethically-significant dimensions. Alternately, philosophers may want to develop AI agents for engaging in dialogue or negotiating on behalf of individuals or interest groups.

The project is not committed from the outset to the idea that AI systems should be incorporated into ethics methodology any time soon or even ever. There are many potential objections to incorporating AI into ethics—e.g. that human individuals bear special duties to perform certain aspects of ethical reasoning or discernment for themselves, that use of AI within certain ethics tasks would reduce the value of those tasks, or that the opaqueness of the AI systems involved mean that humans cannot rely on AI systems for certain purposes.    

In this PhD project, the student will characterize some ways in which AI might conceivably be used to improve ethics methodology and they will develop and defend a position on whether humans should or should not attempt to incorporate AI into ethical methodology in those ways.

This PhD position will be part of the Ethics of Socially Disruptive Technologies (ESDiT) programme, a ten-year international research programme of seven academic institutions in the Netherlands that started in January 2020. This programme has a combined budget of €27 million and is funded by the Netherlands Organisation for Scientific Research (NWO) in the Gravitation funding scheme for excellent research, and by matching funds from the participating institutions. The duration of the programme is from January 2020 to December 2029.  The programme has the aim of achieving breakthrough research in at the intersection of ethics, philosophy, technology/engineering and social sciences, and to position its consortium at the top of its field internationally. A key objective is to investigate how new technologies challenge moral values and ontological concepts (like “nature”, “human being” and “community”), and how these challenges necessitate a revision of these concepts. The programme includes four research lines, “Nature, Life and Human Intervention”, “The Future of a Free and Fair Society”, “The Human Condition,” and “Foundations & Synthesis”. 

This PhD position will be situated within the Foundations & Synthesis research line, and it will contribute to the ESDiT research objective on “ New approaches for ethical assessment and guidance of SDTs” . A fuller description of the Foundations & Synthesis research line, as well as the programme as a whole, can be found through the ESDiT website: https://www.esdit.nl/

You will be embedded within the Philosophy & Ethics group in the Faculty of Industrial Engineering and Innovation Sciences at TU/e. Philosophy & Ethics at TU/e is a vibrant international community, consisting of around 30 members with research interests ranging from philosophy of science and technology to ethics and the philosophy of AI. We have strong cooperation with other departments and the new Eindhoven Artificial Intelligence Systems Institute ( EAISI ). TU/e is part of the 4TU Ethics consortium ( https://ethicsandtechnology.eu/ ), (comprised of TU/e, Delft, Twente, and Wageningen), where we cooperate closely on research and education of students.

The project will be supervised by Elizabeth O’Neill and Philip Nickel.

Please note that there are other vacancies in the Ethics of Socially Disruptive Technologies programme at different participating universities. In case several are of interest to you, we encourage you to apply to them simultaneously.

Job requirements

We are looking for a PhD candidate who has:

• A completed master’s degree in philosophy, preferably in normative ethics, applied ethics, metaethics, ethics of technology, or philosophy of technology.
• Excellent speaking and writing skills in English.
• An aptitude for independent work.
• A background in computer science, especially machine learning, is a plus.

Conditions of employment

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities, scale P (min. €2,770 max. €3,539).
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process .
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A  Staff Immigration Team  and a tax compensation scheme (the 30% facility) for international candidates. 

ESDiT PhD students are encouraged to spend a semester abroad, for which a budget is available to cover expenses; a generous conference travel budget is also available for the position.

Everyone deserves to feel at home at our university. TU/e as well as the larger ESDiT research program encourage applications from women, scholars with disabilities, scholars from minority backgrounds, and other persons from groups that are currently underrepresented in philosophy.

Information and application

Eindhoven University of Technology is an internationally top-ranking university in the Netherlands that combines scientific curiosity with a hands-on attitude. Our spirit of collaboration translates into an open culture and a top-five position in collaborating with advanced industries. Fundamental knowledge enables us to design solutions for the highly complex problems of today and tomorrow.  Curious to hear more about what it’s like as a PhD candidate at TU/e? Please view the video.

Information

Do you recognize yourself in this profile and would you like to know more? Please contact dr. Elizabeth O’Neill, [email protected] . Visit our website for more information about the application process or the conditions of employment. You can also contact [email protected] .

Are you inspired and would like to know more about working at TU/e? Please visit our career page .

Application

We invite you to submit a complete application by using the apply button. The application should include a:

• Cover letter in which you describe your motivation and qualifications for the position
• Curriculum vitae and the contact information of three references
• Brief description of your master’s thesis
• One-page statement elaborating on your ideas for this project
• Writing sample

We look forward to receiving your application and will screen it as soon as possible. The vacancy will remain open until the position is filled. 

Desired start date (negotiable): Dec. 1, 2024

We do not respond to applications that are sent to us in a different way. Please keep in mind you can upload only 5 documents up to 2 MB each. If necessary, please combine files.

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My Top Artificial Intelligence (AI) Stock to Buy Now (and It's Not Nvidia)

• Adobe just raised its full-year guidance and spent $2.5 billion to buy back stock.
• The company has overcome a multiyear-innovation glut and is well positioned to reward shareholders in various ways.
• The valuation is reasonable despite a major concern.
• Motley Fool Issues Rare “All In” Buy Alert

NASDAQ: ADBE

Adobe is monetizing AI in the enterprise-software space.

Artificial intelligence (AI) demands increased computing power, which has been a boon for technology infrastructure and semiconductor companies like Nvidia . These companies benefit from the need to run complex AI models no matter where they come from.

Enterprise-software companies like Adobe ( ADBE 0.68% ) are challenged because they have to prove AI is worth investing in. In other words, users need to like and pay for what Adobe is building. The company's recent results indicate its strategy is working.

Investors cheered Adobe's second-quarter fiscal 2024 financial results and updated full-year guidance -- sending the stock soaring on Friday. The earnings call was, in many ways, similar to the Q1 call. Only this time, Adobe's AI investments translated to impeccable results and high margins.

Even after the run-up, Adobe remains an underrated growth stock to buy now. Here's why.

Image source: Getty Images.

Driving value

It's a mistake to get too caught up in a company's quarterly results. But I think a few years from now, we may look back at this one as a turning point for Adobe.

Document Cloud revenue grew 19% as Adobe added a record $165 million of new Document Cloud annualized-recurring revenue. Digital Experience subscription revenue grew 13% year over year, and Creative Cloud grew revenue 11% on a constant-currency basis. Commenting on its Creative Cloud segment, Adobe management said it experienced "strong renewals as customers migrate to higher-value, higher [average revenue per user] ARPU Creative Cloud plans that include Firefly entitlements."

Adobe has implemented its generative AI tool, Firefly, across its flagship products. It's encouraging to see that Firefly is driving customers to spend more money.

Up until now, Adobe's expenses were outpacing its gross profit . But this quarter, operating income increased at a higher rate than gross profit -- boosting margins and indicating the company is improving its profitability and managing costs. Adobe booked a generally accepted accounting principles ( GAAP) operating margin of 35.5% in the quarter and a non-GAAP operating margin of 46%. For context, Adobe has averaged a GAAP operating margin in the low 30% range for the last five years.

Commercial subscriptions continue to be a standout for Adobe. But the company is also gaining interest and usage for its Express mobile and Express for Business offerings, which is an all-in-one app that leverages AI to help users create graphics, PDFs, and short-form videos.

Longer term, the key for Adobe will be catering to all customers -- commercial, individual, and education -- across all categories. A business may be able to justify a higher price tag and experiment with new tools. However, Adobe needs to find a pricing structure for different markets. Monitoring the adoption of an all-in-one tool like Adobe Express will be a good way to gauge interest in generative AI from individual users, so it's worth following up on future investors' presentations.

Adobe's buybacks are powering earnings growth

Adobe, a cash cow with recurring revenue, can afford to make long-term investments and buy back stock . Its earnings growth can come from net income and reducing the outstanding share count to boost earnings per share.

Adobe's updated guidance calls for non-GAAP earnings per share of $18.00 to $18.20 -- giving it a price-to-earnings ratio of 29 based on its 2024 target and current stock price of around $525 a share. Adobe spent $2.5 billion on buybacks in the quarter. Last quarter, it announced a $25 billion buyback program that runs through fiscal 2028. That level of buybacks is substantial, considering Adobe has a market cap of $235 billion. It also indicates that Adobe has extra dry powder and that its spending isn't out of control.

Another advantage of an enterprise-software company like Adobe is that it doesn't rely on debt to operate the business. Low cost of goods sold and recurring revenue mean that the main costs are operating expenses like sales, marketing, research, and development.

Adobe has more cash and cash equivalents on its balance sheet than long-term debt. And it doesn't pay a dividend. So, when the company generates outsized gains, you can expect it to reinvest those profits back in the business and accelerate organic growth, make acquisitions, or repurchase stock. The capital-light nature of the business is a key advantage compared to leveraged companies that are pressured to use outsized profits to pay down debt.

A major risk worth considering

Analysts have been direct with Adobe management on the last couple of earnings calls. Adobe was grilled about its lack of profitability and weak guidance in Q1. This quarter, there was a focus on enterprise software monetizing AI and the vulnerability of a user-based subscription model.

Arguably, the most important moment from the earnings call was when CEO Shantanu Narayen responded to an analyst question on AI becoming so strong that it reduces the need for larger user-based marketing teams -- in other words, the existential threat of AI generating content on its own, so there is no longer a need for a subscription model based on the number of users. He said: 

If the value of AI doesn't turn to inference and how people are going to use it, then I would say all of that investment would not really reap the benefit in terms of where people are spending the money. And so we're always convinced that when you have this kind of disruptive technology, the real benefits come when people use interfaces to do whatever task they want to do quicker, faster, and when it's embedded into the workflows that they're accustomed to because then there isn't an inertia associated with using it. So with that sort of as a broad segment, I am a big believer that generative AI is going to, for all the categories that we're in, it's actually going to dramatically expand the market because it's going to make our products more accessible, more affordable, more productive in terms of what you -- what we can do.

Narayen is making the case that chip companies have benefited from AI, but the real impact comes from what generative AI can do to improve software applications. That may be true, but even if AI doesn't completely replace marketing teams, efficiency improvements could still lead to fewer software licenses. If one user can accomplish the tasks that used to take two or three users, this can lead to higher revenue per subscriber but fewer overall subscribers.

This isn't an Adobe-specific problem but a concern for all enterprise-software companies that depend on recurring revenue charged by the number of users. Uncertainty regarding whether AI will be a net positive or negative over the long term is one of the biggest question marks impacting the investment thesis.

Think big picture with Adobe

When building an investment thesis, it's important to understand the bear case and why the investment may not work out. A couple of years ago, Adobe's biggest red flag was a lack of growth and innovation. Today, Adobe is returning to growth and has a clear trajectory for monetizing AI, but there's the risk of too much innovation weakening its business model.

It all comes down to which risk you view as greater. Innovative companies usually win out over the long term, and I think Adobe can adapt its pricing model over time if necessary. So, taking a step back, the investment thesis has gotten much stronger, and the financials look better, too.

Adobe is my top AI stock to buy now because I think the valuation is reasonable, and there's untold market potential for building AI creative tools. If Adobe can build tools that can handle a larger share of a marketing campaign or content creation for a social media account, the benefits would be so valuable that they could overcome user-volume declines. It's too early to tell how it will play out, but the risk and potential reward make sense for patient investors.

Daniel Foelber has no position in any of the stocks mentioned. The Motley Fool has positions in and recommends Adobe and Nvidia. The Motley Fool has a disclosure policy .

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Investment thesis

Last summer I was considering buying a new car and an electric vehicle [EV] was a tempting option due to its more attractive total ownership cost. However, at the end of the day, I bought an internal combustion engine [ICE] vehicle due to the uncertainty about the longevity of an EV's battery. It appears that the solid-state battery technology developed by the company named QuantumScape ( NYSE: QS ) might decrease the level of uncertainty regarding the longevity of EV batteries. Apart from the battery longevity, the technology potentially has other advantages over batteries which are currently deployed in the EV industry. However, the commercialization looks far from now as QS is yet to generate revenue and Wall Street analysts do not expect the company to generate profits in the foreseeable future.

I could have said that QS is an interesting investing opportunity in case its valuation was at least comparable with its book value, but the current market cap is two times higher than the company's net assets. Moreover, its $2.5 billion market cap looks ridiculous when we bring Tesla's ( TSLA ) valuation in 2011 to the context. Tesla had comparable valuation when it already generated $200 million annual revenue and its EPS was much closer to zero compared to QS's current bottom line.

Of course, solid-state battery technology looks like a disruptor, but the level of uncertainty regarding timing of its commercialization is extremely high. The stock is sliding, and its sky-high valuation suggests that there is still room for the share price to drop further. I would consider buying QS if its market cap drops by additional 50%, but at the moment it looks like an apparent "Strong Sell" to me.

Company information

QuantumScape is a company developing next-generation solid-state lithium battery technology for EVs and other applications. According to some sources , solid-state batteries have numerous advantages over lithium-ion batteries, which are currently deployed by the EV industry.

vajiramias.com

Volkswagen ( OTCPK:VWAGY ) partners with QS since 2012 . According to the latest 10-K report , the company was founded in 2010 and went public in 2020. The company's fiscal year ends on December 31, and it conducts its business through a sole operating segment.

Financials and business

There is not much to discuss from the financial perspective. The technology is not commercialized, which means that the company is yet to generate revenue.

According to the company's cash flow statement , it burned more than $200 million in cash from operations in each of the last two fiscal years. Raising debt when a company does not generate sales is barely possible, which means that issuing new shares is the only option to finance the company's operations. This is not good for investors, as it dilutes shareholders' value. Since consensus estimates forecast the company's annual revenue to surpass $200 million [operating cash burn rate] not earlier than FY 2027, it is extremely likely that shareholders will see much more dilution in the next couple of years.

The good point is that the company's financial position is decent, with around $900 million in net cash. On the other hand, the business requires exceptional capital allocation from the management because QS will need substantial investments in CAPEX to ramp up production once the technology commercializes. The company invested more than $300 million in CAPEX over the last three years.

Seeking Alpha

The industry where QS operates is promising. A robust secular shift to EVs is expected to help the industry to deliver a 25.1% CAGR over the next decade, a strong tailwind for a company like QS. On the other hand, it is important to understand that the solid-state batteries technology is still scratching the surface and not commercialized. Moreover, it can take years before optimal supply chains are built around the brand-new industry. Raw materials sourcing will also highly likely be a substantial problem to solve before the industry is able to ramp up. That said, the level of uncertainty around the technology's commercialization timing is extremely high.

Despite uncertainty around the technology's commercialization, the industry is already crowded. Apart from QS, there is another public U.S. company involved in the race, called Solid Power ( SLDP ). SLDP has strategic partnerships with prominent automotive players like Ford ( F) and BMW ( OTCPK:BMWYY ). Tesla does not classify its battery enhancements as "solid-state", but this massive EV player is also a formidable potential competitor to QS.

The industry is evolving rapidly outside the U.S. as well. Toyota ( TM ) pours billions into next-gen batteries. Mercedes-Benz ( OTCPK:MBGAF ) partners with Taiwanese solid-state battery startup called ProLogium. In South Korea, developing next-gen batteries is above corporate level, as it was the country's government's decision to spend $15 billion on R&D to develop more efficient batteries. China also has massive ambitions to develop next-gen EV batteries. That said, the industry which is still not commercialized already looks extremely competitive.

To conclude this part, there are no apparent strategic strengths behind QS's business apart from its partnership with Volkswagen. I could have called industry tailwinds as a potential catalyst, but it is extremely crowded with numerous prominent players willing to pour billions into developing next-gen batteries.

QS declined by 31% over the last twelve months. A major part of this was recorded in 2024 since QS delivered a 26% YTD share price decline. There is almost nothing to discuss from the valuation ratios perspective because QS does not generate revenue yet. The only ratios available are the TTM and forward price-to-book ratios, which look elevated. I consider these levels high because paying two times the book value of the company's net assets look expensive for the extreme level of uncertainty QS offers to investors.

I cannot simulate the DCF model for QS because consensus estimates forecast the adjusted EPS to become positive, not earlier than 2030, which is six years from today. Forecasting that far means extremely high level of uncertainty and the DCF model relying on this assumption will be extremely not reliable, in my opinion.

To highlight QS's overvaluation, I need some reliable context. The company's current market cap is around $2.5 billion. Despite loads of controversy around Tesla's current valuation and Elon Musk's endeavors, I think that it is difficult to object to the fact that Tesla is one of the biggest disruptors in the 21st century. Therefore, I want to show readers what Tesla's financials looked like when it had about the same valuation as QS has at the moment.

As shown in the below picture, Tesla already generated $200 million annual revenue and its adjusted EPS was moving closer to zero. That said, Tesla had almost the same market cap when it was already obvious that EV technology is not only disruptive but also commercially viable. In this context, QS's current multibillion valuation with zero revenue and far lower EPS looks ridiculous. Let us also not forget the difference in addressable markets of these two companies. Global automotive market is a multi-trillion industry , while batteries is just a crucial but still auxiliary industry, with the size still below $100 billion .

Risks to my bearish thesis

Despite the extreme uncertainty about the economic viability of solid-state battery technology, I cannot deny that various scientists suggest that it is indeed a much more efficient solution than batteries which are deployed at the moment. Therefore, the technology is disruptive, and any disruptor can potentially fly. Even Tesla faced significant going concern risks in its early years, but it eventually ended up becoming by far the largest automotive company by market capitalization. That said, the possibility that QS can indeed change the world with its new battery technology is above zero.

Moreover, we should not forget that QS has a solid strategic strength in the form of its long-lasting strategic partnership with Volkswagen, which is one of the largest car manufacturers in the world by volume. Volkswagen sells around 9 million cars per year , and expects EV models to reach 50%-70% of its total deliveries by the year 2030. Therefore, QS has a massive potential client for its solid-state batteries, which will sell around 5 million EVs per year by 2030.

Apart from fundamental reasons, it is also important to recall that stock's price is driven by the supply-demand equilibrium. The technology is disruptive and can potentially fly. There are not so many public solid-state battery companies in the U.S. stock market, as I understand. Therefore, the demand for QS stock might be elevated just because there are almost no alternatives to get exposure to the U.S. solid-state battery exposed company.

Bottom line

To conclude, QS is a "Strong Sell". It is still too early to say that solid-state battery technology is economically viable and even if it is, the space already looks extremely crowded with several major automotive companies already in the technological race. Buying at a $2.5 billion valuation a company with zero revenue and no certainty regarding the economic viability of the business looks like a big overprice.

Editor's Note: This article discusses one or more securities that do not trade on a major U.S. exchange. Please be aware of the risks associated with these stocks.

This article was written by

Analyst’s Disclosure: I/we have no stock, option or similar derivative position in any of the companies mentioned, and no plans to initiate any such positions within the next 72 hours. I wrote this article myself, and it expresses my own opinions. I am not receiving compensation for it (other than from Seeking Alpha). I have no business relationship with any company whose stock is mentioned in this article.

Seeking Alpha's Disclosure: Past performance is no guarantee of future results. No recommendation or advice is being given as to whether any investment is suitable for a particular investor. Any views or opinions expressed above may not reflect those of Seeking Alpha as a whole. Seeking Alpha is not a licensed securities dealer, broker or US investment adviser or investment bank. Our analysts are third party authors that include both professional investors and individual investors who may not be licensed or certified by any institute or regulatory body.

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Ryan Wong is an engineer-turned-CEO of Visier , a people analytics company.

Fun fact: I was part of the generation that helped build the internet. Al Gore landed in hot water for making a similar claim , so let me explain.

At college back in the early 1990s, I wrote my thesis on network bulk transfer protocol, a technology for transmitting multimedia data like audio and video online. When I told colleagues and friends how the internet would change the world, they responded with disbelief.

Fast-forward to the present, and the internet is an essential service. Life without it is incomprehensible.

The same thing is happening with AI but at lightning speed. And in few places is this more apparent than human resources.

HR departments already face pressure to adapt to sea changes in the way we work, partly thanks to the pandemic’s upending of traditional office life. With the right talent in short supply, CEOs also increasingly expect HR to drive change and link people with business results.

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AI adds fuel to the fire. This hugely disruptive technology is driving a generational shift in the world of work, forcing companies to rethink some roles and replace others (paywall) . But although HR leaders may see AI as a threat, it also represents a solution to the new way of work—a powerful tool to connect the dots between people and impact.

Ultimately, HR can’t afford to ignore AI. As the founder of a company that helps businesses harness their people data, I still see HR departments sitting on the sidelines. Big mistake. Here’s why HR needs to embrace AI—and how to make the most of it.

1. AI helps source talent, faster.

When it comes to sourcing talent, HR departments have their work cut out for them. The labor market remains tight, with 8.5 million jobs unfilled in the U.S. Three-quarters of employers say they have trouble filling positions. On top of that, AI has blindsided HR teams by swiftly making some workplace roles obsolete.

Luckily, AI can help here, too. One simple use case: Although it may sound hard to believe, recruiters still spend much of their time screening resumes. At an organization with hundreds or even thousands of open positions, that can be a monumental task. This is a perfect job for AI, which can enable HR teams to get through the slush pile faster and refocus their energy on interviewing the most promising candidates.

More broadly, AI represents a superpower for HR teams wrestling with dramatic changes to the workforce. With roles transforming nearly overnight, it’s critical for companies to have a complete catalog of employee skills, not just titles.

AI can help assess and identify skills across the organization, matching them against unfilled roles. As a result, HR can make smarter choices about reskilling, reassigning or hiring talent.

That shift pays off. Organizations taking a skills-based approach are about twice as likely to place talent effectively and keep high performers around.

2. How AI helps solve HR’s toil problem.

The grim reality: Most HR departments still function as the employee help desk. Each day, they’re inundated with reactive questions and problems. The same things crop up again and again—salary bands, vacation days and medical and dental benefits. Typically, answering those questions calls for a manual search. With the pressure on HR to deliver business results, this isn’t an effective use of anyone’s time.

AI offers relief from that tedium. The right platform can turn all the information that employees regularly seek into a self-service function or put the answers at HR’s fingertips.

This frees up HR teams to focus on the art of the profession—human relationships—rather than the mechanics of it. Less time on service requests means more time for conversations about managing change and closer attention to employees’ health and well-being.

Deploying AI also gives HR leaders the time and mental bandwidth to delve into strategy. Liberated from repetitive toil, they can address the real question on every CEO’s mind: Do we have the right people to drive the business?

For HR, embracing these changes can save time and money. By using AI to automate HR tasks, IBM saved 12,000 hours in 18 months.

3. AI unlocks people insights that drive the business.

But the greatest potential of AI is helping connect the dots. HR teams are sitting on a gold mine of data about how people impact business results. Who are the top performers? Why is turnover so high? Who’s contributing to customer retention? The answers are in the data—but tapping it is another story.

For starters, even inside HR departments, data is often locked inside spreadsheets and tables, inaccessible to anyone without an analytics background. Getting HR to share data is another hurdle— only 3% of managers say they have all the people data they need. And even if they do get their hands on the data, managers might not be equipped to analyze it either.

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AI also helps HR get insights into the hands of people who need them. Take something as foundational as pay. For managers, knowing the right salary to pay an employee is actually exceptionally complex, requiring analyzing industry standards, performance reports and company guidelines. In the end, many compensation decisions are based on gut instinct rather than analysis and are open to bias. But today’s smart compensation tools leverage AI to let managers make data-driven decisions that help remove bias from the pay equation while also rewarding top performers at risk of quitting.

When it comes to deploying AI, HR departments must decide what’s right for them. But make no mistake: This technology will have a bigger impact than the internet and mobile revolutions combined. For HR professionals, choosing to ignore AI could mean getting left behind—not tomorrow but today.

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