Will AI Note-Taking Apps Reduce Listener Comprehension?

It’s probably way too early to assess the impact of note-taking apps on listener comprehension and recall. So far, studies of the matter seem to have focused on the difference between manual note taking (long hand) and mechanical (typing) note taking. 

That is perhaps a different comparison than either manual or mechanical notetaking and the use of note-taking apps, which is in the immediate sense the same as “not taking notes,” in terms of the effects on listening or comprehension. 

To the extent that manual note taking enhances listening, automated note taking apps might conceivably lead to less-deep cognitive processing or less conceptual understanding. The reason is that studies on the relationship between manual (longhand) note taking and listening comprehension and memory generally suggest that the slower, manual process encourages deeper cognitive processing. 

The physical act of writing is thought to force the listener to process and select important information rather than simply transcribing verbatim, which also is thought to encourage memory and understanding. 

Since the notes are transcribed, verbatim, the impact on learning, long term, might be improved, it is possible to suggest. 

Study (Year)	Medium/Context	Key Findings Related to Manual Note-taking	Source Link
Mueller & Oppenheimer (2014)	Longhand vs. Laptop Note-taking in Lectures (College Students)	Longhand notes led to better performance on conceptual questions. Laptop note-takers took more notes with greater verbatim overlap, suggesting shallower processing (transcribing without synthesis) was detrimental to conceptual learning.	https://pubmed.ncbi.nlm.nih.gov/24760141/
Flanigan et al. (Meta-Analysis, 2023)	Typed vs. Handwritten Lecture Notes (24 separate studies)	Handwritten notes led to higher academic achievement (Hedges' g = 0.248) despite typing notes resulting in higher volume. Concluded handwritten notes are more useful for studying and committing to memory.	https://scholars.georgiasouthern.edu/en/publications/typed-versus-handwritten-lecture-notes-and-college-student-achiev
Özbay (2013)	Note-taking vs. No Note-taking while Listening (Higher Education)	Note-taking was found to have an impact on comprehension and recall in lectures, rendering listeners more active and engaging them in higher-order cognitive skills (evaluation, interpretation, summarizing).	https://www.researchgate.net/publication/271025457_The_impact_of_note-taking_while_listening_on_listening_comprehension_in_a_higher_education_context
Kusumi, Mochizuki, & Van Der Meer (2024)	Handwriting vs. Typewriting (EEG Study)	Handwriting (manual) showed far more elaborate brain connectivity patterns (theta/alpha coherence) than typewriting, which is known to be crucial for memory formation and learning.	https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2023.1219945/full
Katims & Piolat (2023)	Handwritten Notes and Listening Comprehension (L2 English Learners)	Found that the quality of handwritten notes, specifically the number of 'information units' and a higher 'efficiency ratio' (relevant info/total words), positively correlated to listening comprehension test scores. Total word count was not a factor.	https://publications.coventry.ac.uk/index.php/joaw/article/download/838/983
Dunkel, Mishra, & Berliner (1989)	Note-taking and L1 vs. L2 Listening	For L1 (native language) listening, having notes available during the test was the most beneficial aspect, not the act of taking notes alone. For L2 (second language) listening, the impact of note-taking on overall performance was often non-significant.	https://www.govtilr.org/Publications/Notetaking.pdf

It’s too early to assess the impact of automated note-taking apps, which will require virtually no effort on the part of the listener (either manual or mechanical notetaking). But one suspects the impact will be greater on listening attentiveness than on long-term memory. 

If the core function of manual note-taking is the encoding effect (deep processing required for selection and paraphrasing), then automated apps bypass this active filtering process, leading to a shallower level of processing during the listening event, we might guess.

Since the mental effort required to transform a spoken idea into a concise, manually written note forces the listener to analyze and synthesize the information, the absence of a need to do so could, or should, lead to less thinking as a part of taking the notes.

Which Came First: the Chicken or the Egg?

In many ways, artificial intelligence outcomes are a “which came first, the chicken or the egg?” process.

Some job functions are immediately amenable to AI substitution or augmentation. Software code generation and customer support functions come to mind. 

Job Function	Specific Tasks Enhanced or Automated by AI
Software and Product Development	Code Generation and Completion: Writing, debugging, and testing code, often referred to as "vibe-coding" or "AI-assisted coding." Documentation: Automating the creation of technical and product documentation. Refactoring: Modernizing and improving existing codebases more quickly.
Customer Service/Support	Customer Self-Service: Deploying AI-powered chatbots and virtual assistants to handle a high percentage of customer inquiries 24/7. Agent Support: Providing real-time knowledge assistance and suggested responses to human agents, reducing response time and improving resolution quality.
Sales and Marketing	Personalization: Generating highly personalized content, email marketing, and product recommendations at scale. Content Creation: Drafting marketing copy, social media posts, and campaign templates more efficiently. Lead Scoring & Forecasting: Using predictive analytics to identify high-value leads and forecast sales trends.
Human Resources (HR) & Recruitment	Candidate Screening: Automating resume parsing and initial candidate screening. Administrative Tasks: Scheduling interviews and managing administrative workflows.
Data Analysis & Business Intelligence	Data Collection and Processing: Automating the collection, cleaning, and organization of vast datasets. Insight Generation: Applying complex algorithms to find actionable insights, predict trends, and inform strategic decisions (Predictive Analytics).
Administrative and Office Work	Content Drafting: Writing emails, summarizing documents, and generating reports (Generative AI). Scheduling: Managing and optimizing complex scheduling and calendaring. Meeting Assistance: Real-time transcription, summarization, and action-item extraction from meetings.

Likewise, some industries already seem to be making routine use of AI. As typically is the case, financial services, advertising and e-commerce industries have been early adopters. Fraud detection is a major financial services use case, where personalization is huge in e-tailing and content industries. 

Industry	Primary AI Applications
Financial Services / FinTech	Fraud Detection: Real-time analysis of transactional patterns to identify and prevent fraudulent activities. Risk Management: Using advanced analytics for credit scoring, algorithmic trading, and personalized financial planning. Customer Service: AI-powered chatbots and virtual assistants for customer inquiries and automated banking tasks.
Healthcare and Life Sciences	Diagnostics: Analyzing medical images (e.g., X-rays, MRIs) for earlier and more accurate disease detection (e.g., cancer, heart disease). Drug Discovery: Accelerating research and development by identifying high-potential drug candidates and simulating molecular interactions. Operational Optimization: Streamlining hospital workflows, scheduling, and inventory management.
Technology and E-commerce/Retail	Personalized Recommendations: Analyzing user data to provide tailored product and content recommendations (e.g., Amazon, Netflix, Spotify). Inventory & Supply Chain: Sales and demand forecasting, warehouse automation, and dynamic pricing models. Cybersecurity: Real-time threat detection and response.
Manufacturing	Predictive Maintenance: Monitoring equipment and using AI algorithms to predict failures before they happen, reducing downtime and maintenance costs. Robotics: Integrating AI to enhance robot functionality for autonomous tasks in assembly lines and warehouses.
Logistics and Transportation	Route Optimization: Analyzing real-time data to find the most efficient delivery routes and traffic management. Autonomous Vehicles: Developing the software for self-driving cars and drone deliveries. Supply Chain Management: Predicting demand and managing inventory efficiently.
Education	Personalized Learning: Creating adaptive learning experiences that adjust to each student's pace and style. Administrative Efficiency: Automated grading and feedback for certain assignments.

Also, higher-performing entities tend to produce measurable gains first because they are better-performing entities overall. 

Such firms have the embedded processes that allow them to take advantage of new technologies faster. It’s likely worth keeping that in mind when we try to assess where AI is having the most impact. 

Anthropic's Economic Index takes a look at where Claude is being used, and for what purposes, by consumers and businesses across the world.  

Education and science usage shares are on the rise, while the use of Claude for coding continues to dominate the sample at 36 percent of total instances. But Claude use for  educational tasks increased from 9.3 percent to 12.4 percent, while use for scientific tasks from 6.3 percent to 7.2 percent.

Anthropic also notes a shift towards autonomy. “Directive” conversations, where users delegate complete tasks to Claude, grew from 27 percent to 39 percent. The study also notes increased use in coding (+4.5 percentage points) and a reduction in debugging (-2.9 percentage points). 

But we might also note the difference between correlation and causation, as there will be a tendency for value chain suppliers to argue that AI usage “produces” or “causes” observed performance gains (revenue, income, profit margin, productivity). 

In fact, quite the opposite could be happening. High AI usage occurs in industries, countries or by individuals who are already wealthy, well educated and working in settings where cognitive or intangible products are an important part of the output. 

In other words, high AI adoption follows firm and industry success, rather than “causing” it. It’s similar to the “correlation versus causation” argument we might have about home broadband “causing” economic development. 

Some might note that high-quality home broadband tends to be deployed in areas of higher density, higher wealth, higher income and higher education. Quality home broadband (“fastest speeds”) does not cause the wealth, income or educational attainment.

Rather, such characteristics create the demand for such services. 

source: Anthropic 

Many studies have noted the tension between correlation and causation when evaluating the impact of new technologies. 

• Acemoglu et al. (2023) “Advanced Technology Adoption: Selection or Causal Effects?” Firms adopting advanced technologies had higher productivity before adoption, suggesting selection effects rather than pure technological causationLongitudinal firm-level analysis using Census dataPre-existing firm characteristics → Technology adoption

• Autor, Levy & Murnane (2003) “The Skill Content of Recent Technological Change” Computer adoption correlated with pre-existing skill demands rather than creating new skill requirements. 

• Caselli & Coleman (2001) “Cross-Country Technology Diffusion: The Case of Computers” Countries with higher skilled labor adopted computers faster; computer adoption didn't independently increase skill premiums. 

• Krueger (1993) “How Computers Have Changed the Wage Structure” Workers using computers earn higher wages, but much of the premium reflects selection of skilled workers into computer-using jobs. 

• DiNardo & Pischke (1997) “The Returns to Computer Use Revisited: Have Pencils Changed the Wage Structure Too?” Computer wage premium largely reflects unobserved worker heterogeneity, as similar premium exists for pencil use.

• Beaudry, Doms & Lewis (2010) “Should the Personal Computer Be Considered a Technological Revolution?” Computer adoption followed rather than preceded productivity gains in most industries.

• Forman, Goldfarb & Greenstein (2012) “The Internet and Local Wages” Internet adoption increased wages more in cities with complementary skilled workforce and business services

• Akerman, Gaarder & Mogstad (2015) “The Skill Complementarity of Broadband Internet” Broadband access increased demand for skilled workers but only in firms/regions with existing high skill levels

• Bloom, Sadun & Van Reenen (2012) “Americans Do IT Better: US Multinationals and the Productivity Miracle” Management practices explain technology adoption and productivity gains; technology alone insufficient

• Cariolle (2021) “International Connectivity and the Digital Divide” Submarine cable connections improve economic outcomes primarily in countries with existing institutional capacity

• Hjort & Poulsen (2019) “The Arrival of Fast Internet and Employment in Africa” Fast internet increased employment in skilled jobs but decreased it in unskilled jobs

• Jensen (2007) “The Digital Provide: Information Technology, Market Performance, and Welfare” Mobile phone adoption by fishermen improved market efficiency, but required existing market infrastructure

• Aker (2010) “Information from Markets Near and Far” Mobile phone coverage reduced price dispersion only in markets with existing trading relationships

• Duflo & Saez (2003) “The Role of Information and Social Interactions in Retirement Plan Decisions” Retirement plan participation increased after information sessions, but mainly among already financially sophisticated employees

• Kling & Liebman (2004) “Experimental Analysis of Neighborhood Effects on Youth” Moving to better neighborhoods improved outcomes, but families that moved had different characteristics than non-movers

• Malamud & Pop-Eleches (2011) “Home Computer Use and the Development of Human Capital” Home computers had mixed effects on student achievement; benefits concentrated among students with higher initial ability

• Vigdor, Ladd & Martinez (2014) “Scaling the Digital Divide: Home Computer Technology and Student Achievement” Computer and internet access at home had negative effects on student achievement for disadvantaged students

Study (Year)	Subject	Key Findings	Direction of Causality
Bils and Klenow (2000)	The Causal Impact of Education on Economic Growth	Correlation between education and growth may be due to reverse causality; richer, faster-growing states find it easier to increase education spending.	Primarily from economic growth to education, with a feedback loop.
Comin et al. (2012)	How Technology Adoption Affects Global Economies	The rate at which nations adopted new technologies centuries ago strongly affects whether they are rich or poor today. Technology adoption lags account for a significant portion of income differences.	Technology adoption has a long-term causal effect on economic prosperity.
Nazarov (2019)	Causal relationship between internet use and economic development in Central Asia	A unidirectional causality exists from GDP per capita to Internet use, suggesting that economic growth stimulates technology adoption.	From GDP per capita to technology use.

Critical thinking isn't about being critical in the negative sense; it's an intellectually disciplined process of actively and skillfully analyzing, evaluating, and synthesizing information.

In the Agent AI Era, How Often Will We Need to Use "Apps"?

Back in the early days of personal computing, applications were loaded onto individual PCs. In the following client-server era, the apps were mostly loaded on local servers and accessed by individual PC clients. 

In the cloud computing era, apps reside on remote servers. In the agent AI era, functions might be invoked directly from AI apps, without the need for discrete applications. 

We are starting to see glimmers already. 

Will Agentic AI Disrupt Enterprise Software? Maybe the Better Question is "How Much?"

What is the future of enterprise software as artificial intelligence continues to advance, now perhaps shifting into additional roles within enterprise software value chains?

The proximate cause is OpenAI making a direct foray into the application software market, perhaps shifting OpenAI from model provider to a direct competitor in applications including customer relationship management, marketing automation, and sales enablement.

OpenAI now offers its own suite of SaaS applications, including the "Inbound Sales Assistant" and the "GTM Assistant" (Go-To-Market Assistant). Other tools covering front office, middle office, and back office software categories are coming, supporting sales enablement, inbound marketing assistance, customer support, product analytics and finance applications.

All of those efforts, and others certain to come, are part of the evolution of generative AI from chatbot to agent. 

The key issue is how well OpenAI's AI models might enable entities to "build their own custom CRM" or integrate AI into existing CRM systems.

At a high level, this is an example of OpenAI moving into additional roles across a value chain, or “up the stack” in terms of functions. 

Such “AI-native” enterprise software obviously poses a threat to current enterprise software leaders. 

source: Bain

 

At a high level, some argue that, at some point, it might not be necessary to use a specific application at all to accomplish a business task. Think of the concept as AI becoming the "gateway to business knowledge."

It is highly possible the enterprise software industry is in a shift from traditional software applications to a model where AI agents handle routine, end-to-end tasks autonomously, essentially bypassing the need to use specific enterprise software for such purposes. 

Among other practical impacts, such mechanisms call into question the traditional license-based enterprise software models, in terms of magnitude if not role elimination. 

source: Bain

Whether there is an AI “financial bubble” or not, the reason for the investment is obvious. AI might be the most-impactful new technology since the internet, with equally-disruptive effects on many industries and firms. 

Enterprise software is but one example of the process at work.