Contents
Major consultancies, research hubs, and academic institutions have thus far proven instrumental in helping businesses and broader society build a data-driven understanding of the dynamics of AI usage and adoption, spanning both enterprise and personal applications. However, it’s only recently that frontier AI labs, specifically Anthropic and OpenAI, have formally released their first wave of large-scale AI usage data, providing us with direct insights into how people and businesses leverage state-of-the-art AI systems.
Consequently, as the first part in a multi-part series on how AI is actually being used, this post will begin by examining and extracting key insights from Anthropic and OpenAI’s 2025 AI usage reports. Next, we’ll present a wealth of research findings that we’ve consolidated by scanning Reddit communities and conversations, using a range of deep research tools, although primarily relying on Perplexity AI (this research comes with some important caveats, discussed later on). In our next post, we’ll analyze our findings and those of OpenAI and Anthropic, examining their implications to cement the foundation for all future discussions this series aims to address.
Here, our only goal is to communicate the data (which is rich), not comment on it. This is an especially long post, so we recommend that readers take a few breaks while sifting through it.
Anthropic & OpenAI: AI Usage Trends
We’ll highlight key findings from Anthropic and OpenAI’s report individually, beginning with the former. For readers interested in reading the full reports/papers, links are provided below:
The Anthropic Economic Index Report
Executive Summary: This report analyzes the evolving global and enterprise adoption of Anthropic’s Claude, finding that AI usage has expanded rapidly despite remaining geographically and sectorally uneven. Educational and scientific tasks are growing quickly, although coding remains dominant. Higher-income and innovation-driven nations lead in per capita adoption, whereas developing economies lag significantly, suggesting a widening global digital divide. Enterprise data from Claude’s API shows that the majority of business usage involves automation, with firms primarily favoring higher-cost, higher-value tasks.
Key Findings
- Coding remains the most popular task, representing 36% of total Claude use, though, contrary to expectations, tasks centering on novel code generation are more frequent than those for debugging/error correction.
- The proportion of education and science-related tasks has increased substantially, from roughly 9% to 12% and 6% to 7%, respectively, implying a consistent growth pattern across knowledge-heavy domains.
- The quantity of tasks tied to business operations and functions has decreased by 2 to 3 percentage points.
- The release of new capabilities (e.g., search, research) guides emerging usage patterns (i.e., research tasks grew following the release of research mode in April 2025).
- User confidence in autonomous task handling is building. 39% (previously 27%) of interactions now reveal automation patterns, and Anthropic suspects this can be attributed to a few different factors: (1) model capability advancements, (2) increased user trust, and (3) user base changes.
- Newly introduced capabilities don’t just provide immediate task-specific benefits; they also enable new task trajectories.
- Augmentative uses, defined by iteration, learning, and validation, although still prevalent, are decreasing in frequency and are now, for the first time, eclipsed by automation tasks.
- Internationally, increases in countries’ per capita usage correlate with shifts from automation to augmentation-style use.
- Wealthy (and primarily Western) nations showcase the highest per-capita usage, relative to their population size, whereas the inverse is true for countries with comparatively less developed economies.
- The US accounts for almost 22% of global usage (not considering population size), but at the district/state-level, when controlling for population size, Utah and DC exhibit higher per capita usage than California, despite California accounting for roughly 25% of total US-based usage, followed by New York (9.3%) and Texas (6.7%).
- In the US alone, per-capita usage varies widely state-by-state; however, usage trends appear strongly correlated with local economic dynamics (e.g., California → high number of IT-related requests, D.C. → document editing, job applications). Moreover, although income disparities don’t neatly explain state-by-state differences, adoption does appear to accelerate in accordance with increased income.
- Globally, per capita usage (adjusted for population size) is led by small, wealthy nations, with Israel at the top, followed by Singapore, Australia, New Zealand, and South Korea. Low and middle-income nations like Bolivia, India, and Nigeria have some of the lowest per capita usage rates, even though India accounts for approximately 7% of global usage.
- High-usage per capita nations exhibit both more diverse usage trends (spanning multiple task domains, with a less salient focus on coding/computer-related tasks) and a stronger focus on augmentation over automation (i.e., using AI collaboratively vs. delegating tasks).
- Anthropic notes that for every 1% increase in GDP per capita, a corresponding 0.7% increase in per capita Claude usage materializes. For the US, a 1% increase in state GDP per capita correlates with a 1.8% increase in usage.
- For enterprises, coding remains prevalent for both Claude and the Claude API, for which it constitutes the majority of use. However, educational and writing tasks eclipse coding tasks for Claude.
- Although enterprise use is quickly accelerating, adoption remains relatively nascent across the business landscape.
- API usage is decisively automation-centric, accounting for 77% of use, contrasting with roughly 50% for Claude. Only 12% of API usage tasks focus on augmentation.
- Approximately 50% of all API traffic stems from software development tasks, whereas 5% is accounted for by AI development and evaluation, 4.7% for marketing, and 1.9% for recruitment.
- The most common API tasks tend to be the most expensive; businesses don’t appear to be concerned about high API costs, focusing more on the gains afforded by the ability to automate high-value tasks. Anthropic estimates that 10% cost reductions would increase usage by a mere 3%.
- API-specific long-output tasks most commonly highlight complex use cases. However, longer inputs provide diminishing returns: a 1% increase in input length leads to a 0.38% increase in output length.
- Building on the point above, businesses may face a significant adoption barrier: decentralized and/or outdated data infrastructures can hinder automation initiatives that require context-intensive approaches.
- For both Claude and the Claude API, capability-task alignment plays a key role in usage: well-aligned tasks exhibit usage rates “orders of magnitude” higher than those that are poorly aligned (e.g., models are excellent at coding tasks).
How People Use ChatGPT
Executive Summary: This study, orchestrated by a team of researchers from OpenAI, Harvard, and Duke, examines how ChatGPT’s global user base employs the chatbot. As of mid-2025, most messages are non-work-related, reflecting steadily growing personal and leisure use. The most common interaction types included practical guidance, seeking information, and writing, which together account for the vast majority of all conversations. Work-related usage centers heavily on writing and decision support tasks, with most tasks linked to obtaining, documenting, or interpreting information. The gender gap in use has largely closed, adoption is fastest in low and middle-income countries, and younger, educated professionals dominate work usage.
Key Findings
- In July of this year, ChatGPT’s total weekly active users accounted for almost 10% of the world’s population (approx. 700 million users).
- Early adopters showcase progressively higher usage rates. However, usage rates are steadily building across all adoptor cohorts, from January 2023 to today. Importantly, non-work usage is outpacing work usage.
- OpenAI hypothesizes that early adopters’ accelerated usage trend stems from model capability advancements and their ability to apply current capabilities to novel use cases.
- Gender-based usage gaps have closed since 2023; today, male and female weekly active users (WAUs) comprise nearly equal userbase shares, with female users slightly edging out male users overall.
- Female users showcase a stronger propensity for topics like writing and practical guidance, whereas male users focus more on technical help, information seeking, and multimedia.
- Almost half (46%) of all WAUs are between 18 and 25 years old. Interestingly, the share of work-related messages steadily increases up to the 36-45 age bracket (31.4%), but then declines as age continues to increase: 46-55 (30.2%), 56-65 (27.1%), and 66+ (16.1%).
- From June 2024 to June 2025, the share of non-work-related messages grew substantially from 53% to 73%; roughly 40% of all work-related messages are writing-centric, followed by practical guidance (24%) and information seeking (13.5%).
- The majority (77%) of user conversations center on three topics: practical guidance, information seeking, and writing. Although writing has declined (from 36% in 2024 to 24% in 2025), practical guidance has remained constant (approx. 29%), while technical help has declined (12% → 5%). Multimedia (7.3%) and self-expression (5.3%) represent the next largest usage shares.
- For work-related messages, technical help has also declined (18% → 10%), possibly due to the use of enterprise APIs, which are not covered in this study.
- The majority (two-thirds) of writing-related conversations revolve around three sub-topics: editing/critique, argument/summary generation, and translation. However, education (sub-category of practical guidance) also emerges as a key use case; approximately 10% of all user messages involve tutoring or teaching.
- To build an understanding of user intent, OpenAI splits user queries into three categories: (1) Asking (information/advice seeking), (2) Doing (task-centric), and (3) Expressing (not Asking or Doing). In terms of total shares, Asking queries account for 49%, Doing for 40%, and Expressing for 11%.
- Asking queries most commonly pertain to practical guidance and information seeking, Doing to writing and multimedia, and Expressing to self-expression. Over the last year, however, the share of queries with Asking and Expressing intent has grown much quicker than Doing queries.
- For work-related messages, when sorted by conversation topics (e.g., practical guidance, writing, etc.), Doing accounts for 56% of queries, Asking for 35%, and Expressing for 9%.
- Across user interactions, the most frequent Generalized Work Activities (GWA) include getting information (19.3%), interpreting information for others (13.1%), and documenting/recording information (12.8%).
- For work-related messages only, the most popular GWAs include documenting/recording information (18.4%), decision-making/problem-solving (14.9%), and creative thinking (13%).
- Work-focused ChatGPT usage predominantly concentrates on two major categories: (1) information retrieval, documentation, and analysis, and (2) decision-making, problem-solving, creative ideation, and advice.
- Interaction quality was assessed across three domains: Good, Bad, and Unknown. Good interactions were 3x more frequent than Bad interactions in 2024, becoming 4x more frequent in 2025.
- By interaction topic, self-expression ranks highest, by a large margin, having a good-to-bad ratio of 7.86. Information seeking (4.75), practical guidance (4.37) are the next highest. Among the lowest are writing (3.11), multimedia (2.8), and technical help (1.95).
- By intent, Asking ranks highest (4.45), followed by Expressing (3.87), and finally Doing (2.76).
- Although ChatGPT adoption has skyrocketed globally from 2024 to 2025, low-to middle-income nations exhibit some of the most prominent growth.
- The level of user education positively correlates with work-related messaging; 37% of non-college-educated user messages are work-related, whereas figures jump to 46% and 48% for users with a bachelor’s degree or post-grad education, respectively.
- Independent of education level, Asking queries account for roughly half of messages. However, college-educated users are 1.6% less likely to pose Doing queries.
- Professional, white-collar users more frequently send work-related messages, and are also more likely to pose Asking queries. This latter trend is especially pronounced in scientific and technical fields (47% of work-related messages).
- For users specializing in business and management, 52% of work-related messages are writing-focused. For IT professionals, 37% of work-related messages center on technical help.
- Overall, work-related interactions predictably vary by topic and occupation: use is concentrated on the central tasks required for a given profession. In terms of GWAs, however, decision-making and problem-solving represent one of the most popular use areas across occupations.
- For work usage, professionals appear to find value in both automation and augmentation, frequently using ChatGPT as an advisor/research assistant.
Reddit: AI Usage Trends
Executive Summary: Using a variety of frontier AI deep research functions (primarily Perplexity AI), we launched a comprehensive investigation into AI usage patterns across Reddit communities, analyzing hundreds of Reddit sources and synthesizing insights from an estimated 1,000+ Reddit conversations. The research timeline spans from January 1, 2024, to October 23, 2025, striving to capture authentic, real-world usage patterns from active Reddit AI users across dozens of subreddits, including r/ChatGPT, r/OpenAI, r/ArtificialInteligence, r/ClaudeAI, r/LocalLLaMA, r/ChatGPTCoding, r/productivity, r/Entrepreneur, and many others.
Research Method
Using deep research features supported by Anthropic’s Claude, OpenAI’s GPT-5, and Perplexity AI, we scanned and analyzed AI usage patterns/trends across numerous AI and non-AI communities. By doing so, we aim to build a more holistic, nuanced, and relatively raw understanding of how the average person uses AI today. To grasp the basics required for using Frontier AI deep research features effectively, we recommend reading this AI experiment, which was published during the initial days of deep research (i.e., some insights may be outdated now).
Our analysis leveraged systematic searches across Reddit using targeted queries focusing on AI usage discussions. The research methodology involved:
- Primary Source Analysis: Direct examination of 270+ Reddit posts and their comment threads.
- Secondary Source Synthesis: Analysis of referenced discussions, linked threads, and community recommendations.
- Cross-Community Validation: Verification of trends across multiple subreddits, to confirm existing trends.
- Temporal Analysis: Tracking the evolution of usage patterns across the 22-month period originally stated.
- Quantitative Coding: Categorization and frequency counting of mentioned use cases, to extract relevant usage trends among active AI users.
- Satisfaction Assessment: Evaluation of user sentiment and reported satisfaction levels in light of revealed usage patterns and trends.
The final dataset encompasses 736 analyzed sources with an estimated reach to 1,000+ unique Reddit conversations, providing additional depth into AI usage patterns and revealing trends potentially uncaptured by frontier AI developers and other major research actors.
As readers navigate our findings, we urge them to keep the following caveats in mind:
- Although we used Claude and GPT-5 for research, the majority of our research (approx. 80%) was conducted with the help of Perplexity; both GPT-5 and Claude struggled with retrieving source material directly from Reddit.
- We primarily used Perplexity for analysis of deep research findings; the statistics and trends we illustrate must be interpreted with caution. This applies to all AI-powered analysis, at least until results are validated by humans and/or other AI systems.
- Although we do not include our deep research prompts here, we did use roughly equivalent foundational prompts across all models used. These prompts were comprehensive and meticulously detailed, establishing research context and intent, scope/research direction, validation mechanisms, step-by-step methodology, and required forms of analysis. Our prompts underwent multiple rounds of iteration, using previous cross-model outputs to drive improvements.
- These findings are exclusively informed by Reddit; we did not extend research to any other knowledge domains, and did not use external research conducted on Reddit by independent researchers/groups.
- Reddit sources explored are limited to those communities/threads that are open-access and currently available (deleted posts are inaccessible). Readers will note some substantive overlap between personal and business domains.
Bottom Line: These findings should be interpreted with caution; they do not qualify as genuine empirical evidence, and we have not attempted to replicate them to demonstrate empirical validity. However, this does not mean that our findings are unimportant or non-useful—on the contrary, much of what we’ve discovered appears to build on Anthropic and OpenAI’s reports, while also adding some nuance.
Findings
We’ve split findings into several categories and sub-categories:
- Major use cases, split by personal and business use domains. We also include adoption & satisfaction scores, use-case-specific tool preferences, applications, and pain points where possible.
- The most prevalent limitations, concerns, and criticisms expressed by Reddit AI users.
- The range of future outlooks that Reddit AI users exhibit.
Major Use Cases (Personal & Business)
Personal Use Cases
- Coding & Programming: Software developers represent one of the most active and satisfied AI user cohorts on Reddit. Our research reveals that nearly 9 out of 10 programming-focused Redditors use AI, and users often describe a “collaborative dance” with AI models, with many frequently initiating new chats rather than persevering in long interactions (to avoid context degradation).
- Adoption Rate: 87%.
- Satisfaction Score: 8.2/10.
Popular Applications:
- Code Generation & Debugging: Users report generating entire functions, API endpoints, and boilerplate code almost instantaneously. Many also claim that AI has helped them expedite error resolution processes; what used to require hours of web search now requires only a few minutes.
- Learning New Technologies: Many junior developers and professionals (coders and non-coders alike) interested in fast-tracking career changes indicate sustained interest in leveraging AI as a 24/7 tutor, particularly for accelerating the acquisition of new technological skills.
- Documentation & Testing: Within the coding community, automation potential is emphasized, particularly when it comes to automating tedious work (e.g., parser writing, SSH problems).
Tool Preferences: Preferences vary, but there’s a clear and concentrated focus on frontier AI tools.
- ChatGPT: Seen as the most versatile and well-suited for explanations and general coding.
- GitHub Copilot: Regarded as the best tool for inline code completion and real-time assistance.
- Claude: Typically preferred for longer, more complex code with better context retention, and regarded as one of the best tools available.
- Cursor: Emerging as a favorite integration for AI coding, quickly gaining popularity.
Pain Points & Limitations:
- Context Loss: Models can sometimes forget important details or information as a session’s context extends.
- Hallucinated Code: Models generate code that “looks good” but doesn’t actually work (this is a popular complaint).
- Dependency Concerns: Many users worry that they’re overrelying on AI for code generation and debugging, expressing skill atrophy concerns.
- Quality Degradation: Occasionally, users report that certain models become worse over time for specific coding tasks that they were once quite good at.
- Mental Engagement Concerns: Interestingly, some users have begun reporting that prolonged work with AI eventually elevates cognitive fatigue, claiming their minds don’t feel as “engaged” or “activated” as they once were.
- Research & Information Gathering: Although research use exhibits the highest satisfaction scores across all categories, users make a point to emphasize that how one uses AI for research matters. The most successful researchers articulate multi-step processes, beginning with broad queries, followed by targeted follow-ups, then cross-referencing with primary sources, and finally, synthesis and organization of findings.
- Adoption Rate: 79%
- Satisfaction Score: 8.4/10.
Popular Applications:
- Deep Web Research: Many users express praise for Frontier AI’s deep research features, claiming that they significantly accelerate the ability to obtain direct answers to questions that would usually necessitate substantial web and/or database searching.
- Literature Reviews & Information Synthesis: Academically-inclined users extensively discuss AI for systematic literature reviews, frequently sharing their prompts and workflows openly. They also cite high-impact research benefits like the ability to synthesize disparate sources cohesively and bridge cross-disciplinary knowledge gaps.
- Fact-Checking & Verification: Although research-focused users often employ AI for preliminary fact-checking, they appear to be well-aware of hallucination risks, regularly emphasizing verification; AI use is characterized as an “intelligent starting point.”
Tool Preferences:
- ChatGPT: Most popular for general research queries, though sometimes critiqued for struggling with complex/multi-step research queries.
- Perplexity: Largely considered to be one of the best AI research tools available, and specifically praised for sourced answers and citations.
- Claude: Preferred for analyzing long documents and academic papers, paralleling context-related preferences expressed among coding communities.
- NotebookLM: Growing adoption for research synthesis and “podcast” generation.
Pain Points:
- Hallucinations: Users continuously recognize that mistakes remain inevitable, at least at this stage of AI advancement (we’ll see this concern in many other areas).
- Outdated Information: Training data cutoffs and current events awareness are perceived as core limitations; AI can’t be trusted for the most up-to-date information.
- Citation Accuracy: AI-generated citations frequently contain errors, despite how convincing they appear; fake citations can be hard to spot when embedded within complex research tasks.
- Depth Limitations: AI is great at providing systematic overviews and dissecting complex source material, but it still misses nuanced insights and can sometimes misinterpret ambiguous ideas or concepts.
- Writing & Content Creation: Writing assistance represents one of AI’s most ubiquitous applications, and across most application domains, we’ve observed a “starting point” paradigm, where users treat AI outputs as drafts requiring human refinement. However, numerous concerns have been raised around the authenticity of AI-generated content and the ease with which human readers can detect it.
- Adoption Rate: 82%.
- Satisfaction Score: 7.8/10.
Popular Applications:
- Email Drafting: The single most common use case, especially among sales professionals who aim to accelerate email outreach to prospective clients and customers.
- Long-Form Content: Centers primarily on in-depth blog posts, articles, and essays, which range between 3k and 5k words.
- Academic Writing: Students have reported using AI extensively for academic essays and reports, raising serious concerns around academic integrity.
- Professional Documents: Reports, proposals, and presentations, often developed by or intended for managers and the C-suite. Custom GPTs have become especially popular here.
- Creative Writing: Story development, character creation, plot assistance. Writers tend to leverage AI for initial brainstorming and prefer to move away from it once the stage is set.
Tool Preferences:
- ChatGPT: Seen as the most versatile for diverse writing types.
- Claude: Usually preferred for longer content and high creative potential.
- Grammarly: Specifically used for grammar and style refinement.
- Jasper: Increasingly used by marketers for commercial content.
- Learning & Education: Although the benefits of personalized learning and education are widely discussed, academic integrity concerns remain at the forefront of these discussions; most teachers have chosen to adapt and integrate AI into the classroom setting, while some continue to reject it, arguing that it qualifies as plagiarism while also compromising student learning.
- Adoption Rate: 71%.
- Satisfaction Score: 7.9/10.
Popular Applications:
- Concept Explanation & Study Guides: Students frequently use AI to break down complex concepts, while also utilizing emerging features for personalized learning experiences; some students report creating personalized practice problems with step-by-step explanations and subject-specific study guides, including flashcards and summaries of course materials.
- Homework Assistance: Possibly the most widespread application, but deeply controversial. Most teachers still struggle to understand where and how the line should be drawn in this context, and worry that students will lose their critical thinking skills as reliance builds, even while recognizing how important it is to embrace AI.
- Language Learning: Beyond students, users engage with purpose-built language learning tools and AIs that support voice interaction to learn and practice new languages. Personal anecdotes often reference the ability to learn new languages without judgment.
Tool Preferences (primarily among students):
- ChatGPT: Typically employed for explanations and homework assistance.
- Claude: Preferred by college-level students for complex topic breakdowns and help across advanced subjects.
- Khan Academy AI: Purpose-built educational tutor, used by students of all ages.
- Duolingo Max: Provides AI-enhanced language learning and is utilized by a global audience.
A potential solution to the academic integrity problem is emerging: redesign assessments to focus on application and synthesis rather than memorization and basic comprehension.
Business Use Cases
- Content Marketing & Copywriting: Marketing professionals arguably represent the most eager cohort of business AI adopters, with social media content creation dominating discussions across communities.
- Adoption Rate: 76%.
- Satisfaction Score: 7.9/10.
Popular Applications:
- Social Media & Blog Content: Marketers extensively use AI for rapid content ideation and variation generation across platforms. Blog article writing and SEO optimization represent core applications, with one marketer reporting a dramatic reduction in sales letter writing time from days to mere hours through GPT-4 integration.
- Ad Copy & Product Descriptions: AI has fundamentally transformed the economics of copywriting variation. Rather than manually crafting dozens of iterations, marketers can now generate multiple versions of Google ads, Facebook ads, and email subject lines in minutes. E-commerce professionals regularly deploy AI for product description generation, though typically on an as-needed basis.
- The Iterative Copy Revolution: What previously required hiring dedicated writers or investing substantial time can now happen almost instantly. Marketers test numerous smaller variations across headlines, search ads, and hook scripts, fundamentally changing how the industry approaches content optimization.
Tool Preferences:
- ChatGPT/Claude: General copy generation remains split, with ChatGPT favored for short-form content while Claude dominates long-form applications.
- Jasper: Purpose-built marketing copy tool experiencing rising adoption.
- Copy.ai: A Specialized copywriting platform gaining popularity among marketing teams.
- SEMrush AI/Surfer SEO: Integrated SEO optimization tools are receiving widespread praise.
The Quality Paradox: Marketers acknowledge a crucial limitation; AI is excellent at producing various types of copy but struggles with determining strategic prioritization. The strategic layer remains firmly human territory, while tactical execution increasingly shifts to AI.
Job Impact Discussions: Extensive debates around whether AI has killed copywriting as a career reveal an emerging consensus: average copywriters face significant displacement while AI accelerates productivity for skilled professionals. Entry-level positions focused on basic content generation face elimination, while strategic copywriters who understand marketing psychology and consumer behavior remain highly valuable.
Pain Points:
- Brand Voice Consistency: Maintaining an authentic brand voice across AI-generated content proves challenging.
- Originality Concerns: Risk of producing generic, templated output that lacks distinctive character.
- SEO Gaming: Over-optimization can trigger search engine penalties.
- Client Expectations: Increased pressure to produce more content faster potentially compromises quality standards.
Best Practices Emerging:
- The most successful marketers feed AI examples of high-performing brand content before generation, use AI primarily for variations and iterations rather than final copy, maintain human strategic oversight throughout the process, combine multiple AI tools for different production stages, and always edit and personalize outputs before publication.
- Business Software Development: Business software development mirrors personal coding applications but operates under significantly higher stakes, with professional developers integrating AI into standard workflows at notable rates.
- Adoption Rate: 68%.
- Satisfaction Score: 8.0/10
Popular Applications:
- Production Code Development: Professional developers describe their AI usage as a collaborative process, noting that AI often identifies subtle inefficiencies and potential bugs that might otherwise go unnoticed during initial development.
- Enterprise Considerations: Unlike personal projects, business software development introduces critical additional requirements. Security reviews become mandatory for AI-generated code, testing and quality assurance processes demand heightened scrutiny, documentation and long-term maintainability require careful attention, and technical debt management becomes increasingly complex.
Tool Preferences: Highly similar to personal coding preferences, with ChatGPT, GitHub Copilot, Claude, and Cursor dominating professional development environments.
Pain Points & Limitations:
- Skill Development Concerns: Growing concerns have emerged around junior developers who heavily rely on AI, potentially lacking fundamental coding competencies. The industry continues grappling with how to balance AI assistance benefits against the necessity of building core programming skills. Some worry that excessive AI dependence during early career stages may create knowledge gaps that become problematic as developers advance to more complex challenges.
- Production Risks: AI-generated code requires thorough vetting before deployment, particularly around security vulnerabilities and edge case handling that models may overlook.
- Customer Service & Support: Customer service AI demonstrates strong business ROI potential, though implementation quality dramatically affects outcomes.
- Adoption Rate: 68%.
- Satisfaction Score: 7.4/10.
Popular Applications:
- Implementation Spectrum: Customer service AI spans a wide capability range. Simple FAQ bots handle common questions and deflect routine tickets.
- Context-aware assistants access comprehensive knowledge bases and reference past customer conversations.
- Sentiment analysis systems automatically route frustrated or angry customers to human agents.
- Multi-language support capabilities enable global scaling without proportional hiring increases.
- Business Benefits: Those working in organizations implementing customer service AI report significant advantages, including true 24/7 availability, instant response times, and enhanced scalability. However, they also reveal that effectiveness can vary dramatically based on implementation quality and organizational context.
Tool Preferences: Various specialized customer service AI platforms, though specific tool preferences vary significantly by industry and company size.
Successful implementations share common characteristics: comprehensive knowledge base training ensures accurate responses, clear escalation paths connect customers with human agents when needed, continuous learning from customer interactions improves performance over time, and realistic customer expectations prevent frustration with AI limitations.
Pain Points:
- The Human Touch Requirement: Complex issues, emotionally-charged situations, and edge cases still require human agent intervention. The most effective implementations strategically blend AI capabilities for routine queries with human expertise for nuanced problems, rather than attempting full automation.
Limitations, Concerns & Criticisms
- Quality & Accuracy Issues: Hallucinations sit at the forefront here, and many users have responded by developing their own verification processes. Crucially, heavy users stress that AI should never be used alone for medical advice, legal guidance, financial decisions, or factual research without verification.
- The Authenticity Problem: In domains such as writing, professional communication, and even dating, users often complain that AI lacks a genuine human voice and that the content it generates is easily recognizable by most. However, advanced users have signaled that eliciting and sustaining this voice is possible once you a) build a strong, experience-based understanding of how models operate, and b) develop a robust prompt engineering foundation.
- Dependency & Skill Atrophy: Users across the board are becoming progressively more worried that their dependence on AI will cause their skills and knowledge to atrophy, rendering them increasingly incompetent. Once more, however, certain cohorts of prolific users express that how you use AI plays a prominent role in the effect it has on your cognitive capability; AI should be leveraged as a collaborator or thought partner instead of an autopilot.
- Job Displacement Fears: From blue to white-collar, many professionals are now uncertain about their professional future, questioning whether they’ll still be able to provide true economic value as this technology advances. There is, nonetheless, some nuance here: AI seems to be raising the bar for excellence, meaning that “good enough” might actually become sub-par. Importantly, the most prevalent job displacement concerns target entry-level, routine work.
- Ethical & Societal Concerns: From AI-influenced suicide to fraud and blackmail, loss of academic integrity, perpetuation of systemic bias, infection of information ecosystems, reveal of sensitive data, and environmental impacts, large-scale ethical and societal concerns are rapidly gaining traction. Most worryingly, however, some users are beginning to recognize that catastrophic risks (e.g., human enfeeblement, ascended economies, dehumanized warfare, mass surveillance) can already materialize with today’s technologies.
Tool-Specific Criticisms:
- ChatGPT: Multiple users report quality degradation over time, context loss in long conversations, and excessive verbosity, repetitiveness, and sycophancy.
- Claude: Free and basic users complain that during heavy use, rate limits are hit quickly, with some users developing guides aimed at establishing elaborate workarounds, though these are typically workflow or task-specific. Concerns about unnecessary and occasionally nonsensical censorship are also fairly popular.
- Gemini: Many exhibit frustrations with inconsistent quality and performance trends, and would even go so far as to claim that Gemini isn’t quite on par with other frontier models. Moreover, although its integration advantages are praised, they aren’t regarded as “good enough” to overcome the aforementioned performance gaps.
- Midjourney & Sora: Heavy users often complain about usage costs, while others find the Discord interface to be rather awkward and clunky. Many users also express that output quality highly depends on how prompts are formulated. Virtually all of these concerns also apply to Sora, though users also reference long wait time and limited availability concerns.
Future Outlooks
Optimistic Views
Our research reveals a strong personal empowerment theme: AI democratizes capabilities previously requiring expensive specialists (e.g., design, coding, writing, analysis, translation), effectively allowing individual creators and small teams to compete with larger organizations. We also observe an increasing interest in using AI for self-upskilling and knowledge expansion, across both personal and business domains.
Reddit users anticipate:
- Continued Capability Expansion: Models becoming more cognitively sophisticated and versatile, better able to handle longer context and ambiguity/uncertainty, exhibiting improved understanding across multiple modalities (notably vision, voice, and video), and producing hallucinations less frequently.
- Better Integration & Cost Reduction: Seamless workflow integration across tools, platforms, and APIs, coupled with an increased number of native integrations. Users also anticipate that as competition builds, prices will correspondingly drop, making increasingly powerful AI systems more accessible, although this doesn’t align with what we see today.
- Specialization: Purpose-built AI tools for specific industries and tasks, the most popular of which include software development, AI testing and evaluation, pharmaceuticals and healthcare, legal and financial analysis, decision-support and strategic guidance, risk forecasting and remediation planning, creative ideation and content generation, and education/personalized learning.
Cautious/Skeptical Views
We also note a pronounced sustainable AI usage theme: Long-term value requires developing healthy AI relationships; that is, relationships defined by the user’s ability to leverage the unique strengths that AI affords within their interest or task domains while maintaining their judgment, learning, curiosity, and skills. In other words, AI is still perceived by most as a tool, not a genuine replacement for human capability, although perhaps an extension of it, particularly when use concerns the development of new skills or knowledge.
Reddit users’ concerns include:
- Overpromising & Underdelivering: The gap between marketing claims and actual capabilities is becoming painfully obvious across most frontier AI deployments.
- Plateau Potential: Researchers worry that current state-of-the-art systems may have already reached their architectural limits, requiring fundamental breakthroughs for the “next leap”; this remains a hotly debated topic.
- Enshittification: Unfortunately, some tools seem to be getting worse over time; users often speculate that companies, especially those operating at the frontier, are more concerned with profit than they are with quality and real-world impact. Certain tools are more prone to this than others; ChatGPT is critiqued more often on this point than Claude, for instance.
- Job Market Disruption: Negative consequences of displacement without adequate transition support; this concern is expressed across virtually all professions to some degree, and white-collar professionals especially are beginning to realize their livelihoods may be under threat.
- Misinformation Amplification & Proliferation: AI isn’t only making it much easier to generate convincing false content at scale but also increasing the likelihood that this content spreads across multiple information ecosystems, perpetuating systemic AI-driven “infections” that might someday lead to information ecosystem collapse.
Adaptation Strategies Emerging
Reddit users have shared some approaches for excelling in the AI era:
- Develop Prompt Engineering Skills: Knowing how to extract the most value from AI isn’t about having access to a multitude of advanced tools, but instead, the ability to master effective prompting through intensive experimentation, cross-model exploration, and real-world application.
- Focus on Strategic Work: Ensure that we limit AI’s power and influence on human strategy, creativity, curiosity, and critical thinking, allowing it to handle the procedural work that stands in the way of us devoting more time and energy to these very capabilities, which are arguably what make us unique as a species.
- Build AI-Augmented Workflows: Integrate AI seamlessly into processes rather than using it as a separate step; this is about allowing the human-AI partnership to flourish, synthesizing human and AI strengths, without introducing the cost of skill and knowledge degradation.
- Maintain Core Competencies: Building on the former recommendation, we should utilize AI as an assistive or expansive force while constantly reminding ourselves that our fundamental skills warrant preservation. In other words, it’s still worth learning the skills required to perform a specific task, even if AI can already do it; AI can be a monumental force for learning in this context as well.
- Specialize Strategically: Become an expert in areas where AI struggles and use AI in areas where you’re an expert. This way, you can increase the probability that you’ll continue to create value as AI advances while relentlessly building on the expertise you’ve developed throughout your life.
Conclusion
We’ve covered a massive amount of information in this post to ensure that subsequent discussions on our topic of interest (how AI is being used) will be robust; fret not, however, the remaining posts in this series will not be as dense. Nonetheless, our next post will explore precisely what all this information means, and more importantly, what it may reveal about the future trajectory of general AI usage.
For those interested in examining other topics spanning AI innovation, governance, strategy, safety, literacy, risk management, and ethics, we recommend following Lumenova AI’s blog.
For those with a targeted focus on enterprise AI governance and risk management, we invite you to check out Lumenova’s responsible AI platform and book a product demo today.