But the data tells a different story.

I wanted to understand how workers in high-stakes settings were using AI in ways tied to valuable outputs, and what was actually driving that use, so I looked at healthcare and coded and analyzed their interviews using the Anthropic Interviewer dataset (Handa et al., 2025). This is a large-scale, publicly released dataset of 1,250 professional interviews tracking how AI is being incorporated into real-world occupational tasks across the economy, available at huggingface.co/datasets/Anthropic/AnthropicInterviewer.

Here’s what I found:

92% expressed positive sentiment toward AI. 69% demonstrated high self-reported reliance on it. And not one individual expressed full trust in AI’s accuracy.

Positive sentiment. High use. Zero full trust.

This is the behavioral paradox at the center of human-AI collaboration: if trust is a prerequisite for adoption, why are people relying on systems they don’t fully believe?

The clinical professionals in this sample (nurses, therapists, pharmacists, paramedics, physical therapists, and medical coders) are not a fringe group of early adopters. They’re people managing real caseloads under significant pressure, and they are using AI in consequential moments: checking a medication interaction with a patient already in the room, generating treatment plan objectives before a session, and navigating a knowledge database during a live call.

At first glance, the healthcare population looks like an adoption success story. They feel good about AI and they’re using it at high rates — but they don’t actually trust it, and the reasons they give in interviews have nothing to do with reliability.

If positive sentiment and trust aren’t driving human-AI collaboration, what is?

The Five Mechanisms of Human-AI Collaboration

These five patterns emerged from reading the interview transcripts. They are descriptions of what people actually said when asked why they use AI in their work.

1. Volume. The workload feels unsustainable without AI. One speech-language pathologist put it plainly: if AI can generate 15 usable items, they only need to produce 10 themselves. A canine rehabilitation therapist cut post-session documentation from 40 minutes to 5. In this context, AI is structurally load-bearing — acting as external working memory to manage cognitive overflow.

2. Starting Point. People use AI to avoid the friction of starting from zero. “I just need a starting point” was one of the most common phrases across the clinical transcripts. AI gives raw material to react to, not a finished conclusion, lowering the cognitive barrier to entry.

3. Reasoning. AI functions as an external reasoning partner. A clinical coder described using AI not to get the answer — “I know it’s probably going to be wrong” — but to move their own thinking forward. “AI helps me converse with my own thoughts,” they said. The individual retains executive judgment while AI extends the cognitive workspace — creating a genuine human-AI collaborative workflow.

4. Default by Necessity. In high-stakes work with competing priorities, AI fills gaps left by unavailable colleagues and inaccessible systems. A pharmacist facing a real-time decision with a patient already at the counter — and no way to reach the prescriber for at least a day — turned to an LLM not out of preference, but because nothing else was available in that moment. It becomes a behavioral workaround for broken organizational architecture.

5. Relative Advantage. AI is better than what came before — Google in some cases, a clunky database in others. An OR nurse described reaching for AI when Google couldn’t surface a fast enough answer. A Medicare call center worker described AI surfacing policies that were previously buried too deep to find. A tool doesn’t need to be perfectly trusted to win. It just needs to offer less friction than the alternative.

What unites these five mechanisms is that none of them require trust. They require utility. And utility is evaluated relative to existing conditions like workload, time pressure, and available alternatives. This is what effective human-AI collaboration actually looks like in practice — not a confident handoff to a trusted system, but a pragmatic, iterative negotiation between human judgment and machine output.

This isn’t isolated to this sample. The American Medical Association’s 2024 physician survey found 2 in 5 doctors were equally excited and concerned about AI. Yet physician use still jumped 78% in a single year — from 38% in 2023 to 66% in 2024 (American Medical Association, 2025).

The takeaway? People are using AI not because “readiness” has landed, but because their workload demands it. And the organizations that understand this; that human-AI collaboration is driven by utility, not sentiment, will be the ones that design for it deliberately rather than waiting for trust to catch up.

#HumanAICollaboration #FutureOfWork #AIAtWork #OrganizationalPsychology #AIstrategy

Just like in evolutionary succession, a pioneering force like AI establishes itself in environments that are hospitable and offer low resistance. These “environments” in an organization are departments or teams where the norms are flexible (Gelfand et al., 2007) and barriers to use are low. Hospitable habitats are departments characterized by flexible norms and accessible data (Cohen & Levinthal, 1990) that allow AI to establish a foothold and eventually reshape legacy routines. Inhospitable habitats are departments defined by high structural inertia, which is resistance in the form of rigid protocols or locked-off data that prevent AI from taking root. In this framework, the future of work is reshaped through hospitable habitats first, because they are most capable of absorbing and acting on the knowledge AI introduces. Departments like research and communications have high potential because of their appetite for knowledge and tolerance for experimentation. Other areas will be slower to follow, their norms shifting gradually under the pressure that high-metabolizing areas place on the broader organization.

To foster this process and accelerate AI adoption, leaders must move past static organizational charts and legacy processes, and recognize the newly emerging organizational structure created by what functions AI best inhabits. This emergent map of systemic norms is called The Topography of AI.

Mapping the AI Adoption Landscape: A Topography of AI in the Workplace

To navigate this evolutionary succession, leaders need a diagnostic tool to identify where AI will naturally thrive and where it will stall. We call these functions or departments, “habitats”. The Topography of AI categorizes these organizational habitats along two critical dimensions:

1. Tightness-Looseness (Gelfand et al., 2007): This dimension measures the strength of operational norms and the tolerance for deviation. “Tight” departments rely on strict rules and predictability, while “loose” departments encourage flexibility and experimentation.

2. Absorptive Capacity (Cohen & Levinthal, 1990): This is a habitat’s ability to recognize the value of new information, assimilate it, and apply it to productive ends. In the context of AI, high absorptive capacity means a habitat has the accessible data and the technical readiness required to metabolize new intelligence.

By crossing these two axes, we identify four distinct habitats: The Innovation Garden (Loose/High Capacity), The Precision Engine (Tight/High Capacity), The Fragmented Sandbox (Loose/Low Capacity), and The Compliance Fortress (Tight/Low Capacity). These dictate how AI integrates into the legacy ecosystem.

The Four Organizational Habitats:

The Innovation Garden (Hospitable): This is the ideal starting point for evolutionary succession. With flexible norms and accessible data, the Garden is highly hospitable. AI establishes an immediate foothold here, acting as an experimental partner that quickly metabolizes legacy routines and creates new, highly efficient processes.

The Compliance Fortress (Inhospitable): This habitat is defined by maximum structural inertia. Rigid protocols and locked-off data make it entirely inhospitable. The Fortress rejects the probabilistic nature of AI, viewing it as a threat to systemic reliability rather than an asset.

The Precision Engine (Conditional): In this habitat, the norms are tight, but the absorptive capacity is high. AI is adopted here, but it is not allowed to freely reshape routines. Instead, it is strictly constrained and optimized for high-stakes accuracy, serving as a powerful but highly controlled calculator rather than an agentic force.

The Fragmented Sandbox (Stagnant): Here, the norms are loose and curious, but the habitat lacks the data architecture and readiness to actually metabolize the intelligence. AI is frequently “played with” in the Sandbox, but these isolated experiments never translate into structural change or operational value.

The Dynamics of AI Adoption: Behavior Change and Organizational Performance

Once AI establishes a foothold in the Innovation Garden, it begins to spread. To understand why AI first takes hold in this habitat, we look at behavior change.

According to the Fogg Behavior Model (Fogg, 2009), a new behavior only occurs when three elements converge at the exact same moment: Motivation, Ability, and a Prompt. The Innovation Garden provides the perfect conditions for this convergence.

First, motivation is naturally high due to the department’s loose norms and inherent drive for experimentation. Second, ability is high because the habitat possesses strong absorptive capacity, meaning data is accessible and the team is equipped to use it. Finally, the technology itself acts as the prompt. Because AI is agentic rather than static, it actively invites interaction through conversational interfaces and predictive suggestions.

When these three elements align, the Innovation Garden digests and embeds the new intelligence into daily workflows. This creates a surge in knowledge output and what we call metabolic efficiency. The department begins processing information faster, generating superior outputs, and operating at a new baseline of speed and scale.

However, this localized success can lead to an ecological imbalance within the broader organization. The hyper-productive Innovation Garden can exert performance pressure on adjacent habitats. As the Garden accelerates, the Precision Engine and the Compliance Fortress suddenly appear remarkably slow, rigid, and resource-heavy by comparison.

This performance pressure is the natural process behind evolutionary succession. The Compliance Fortress can no longer justify its structural inertia when a neighboring department is achieving exponentially better results. To survive this ecological shift, the Fortress is forced to adapt. It must either increase its absorptive capacity by unlocking its data, or it must loosen its rigid norms to allow for new workflows. It must evolve to accommodate the new intelligence, or risk having its legacy routines replaced by the newly emerging organizational structure.

Leadership Imperatives: Shaping Your AI Adoption Strategy

To lead an organization through evolutionary succession, leaders must shift from “system administrators” to “habitat cultivators.” Using the Topography of AI as a map, leaders can take the following actions to cultivate the best ecosystem possible for AI:

1. Target the Habitats of Least Resistance: Do not attempt a simultaneous, organization-wide AI rollout. Identify the Innovation Gardens and seed them first. These habitats are the natural early adopters of the topography, and their successful engagement is vital for seeding the S-curve (Catalini & Tucker, 2017). These early adopters are individuals with deep ties in local networks who act as opinion leaders and technology pioneers while facilitating social learning among their peers. By allowing these habitats to establish a foothold first, leaders create the “proof of performance” necessary to break the structural inertia of more rigid habitats.

2. Prioritize “Ability” Over “Motivation”: Traditional training often tries to talk people into wanting to use AI. The more effective strategy is reducing friction within the habitat. Instead of requiring employees to navigate away from their primary tasks to find the AI, leaders should embed it directly into existing day-to-day tools by using browser extensions, integrated icons, or automated plugins so that accessing AI is seamless. The principle is simple: when the tool is placed at the point of need, using it becomes the path of least resistance (Fogg, 2009).

3. Build “Wide Bridges” to the Fortress: The Compliance Fortress cannot be motivated into change through simple pressure. It must be brought into the succession process through wide bridges. Unlike a narrow bridge where only one person shares an idea, a wide bridge consists of multiple, overlapping connections between two different groups. These bridges are vital for “complex contagions”, which are behaviors that require multiple sources of social reinforcement before they are adopted (Centola, 2018). This is because people usually require confirmation from several different peers before they feel safe enough to change their behavior. Design cross-functional teams where “Gardeners” demonstrate utility and “Fortress Guards” design the governance. This multiple-tie approach makes the shift feel like a shared movement rather than a risky individual choice.

4. Design for Imprinting: Imprinting occurs during a brief sensitive period when an organization is uniquely receptive to new foundational norms and roles (Marquis & Tilcsik, 2013). This period represents a high-stakes window to shape the organizational topography before behaviors become rigid and locked in. For an AI-integrated environment to become systemically embedded, these habits must be fostered and encouraged as the new norm during this sensitive period. What takes root now becomes the baseline of the emergent norms, the foundation of the Topography of AI.

Conclusion: Building an AI-Ready Organization

The persistent failure of AI adoption is a challenge best understood through the evolutionary dynamics of an organizational environment. When organizations treat AI as a static tool, they ignore the dynamic, agentic nature of the technology and the behavioral habitats required for it to thrive. By shifting our perspective to the Topography of AI, we can begin to see why top-down mandates fail and why localized pockets of success occur.

The future of work will be defined by who can most effectively allow for the technological and cultural evolution of an organization. Leaders who recognize their departments as distinct habitats and who use the mechanics of behavior to cultivate them will create organizations that are more than just automated. They will create organizations that are metabolically efficient, capable of digesting vast amounts of intelligence, and ready to inhabit the new landscape of the digital age.

#AIAdoption #Futureofwork #AIStrategy #Leadership #OrganizationalChange #AgenticAI #Innovation

References

Catalini, C., & Tucker, C. (2017). Seeding the S-Curve? The Role of Early Adopters in Diffusion. NBER Working Paper No. 22596.

Centola, D. (2018). How Behavior Spreads: The Science of Complex Contagions. Princeton University Press.

Cohen, W. M., & Levinthal, D. A. (1990). Absorptive Capacity: A New Perspective on Learning and Innovation. Administrative Science Quarterly, 35(1), 128-152.

Fogg, B. J. (2009). A Behavior Model for Persuasive Design. In Proceedings of the 4th International Conference on Persuasive Technology (pp. 1-7).

Gelfand, M. J., Erez, M., & Aycan, Z. (2007). Cross-cultural organizational behavior. Annual Review of Psychology, 58, 479-514.

Marquis, C., & Tilcsik, A. (2013). Imprinting: Toward a Multilevel Theory. The Academy of Management Annals, 7(1), 195-245.

Japan began building a relationship with robots a long time ago. Generations of Japanese children grew up with Astro Boy, a robot hero who could feel, fight, and be grieved. That raised a generation to see intelligent machines as companions rather than threats. Researchers at Indiana University Bloomington have documented how Japanese roboticists deliberately designed their technologies to fit these existing cultural expectations, embedding Shinto ideas about spirit in objects and craft traditions about honoring tools directly into how robots look, move, and are introduced to the public (Šabanović, 2014).

That cultural formation produced measurable outcomes. Japan today produces 38 percent of the world’s industrial robots, according to the International Federation of Robotics. Two of the four largest industrial robot manufacturers in the world are Japanese, and Japan holds the second largest industrial robot deployment in the world. To add another layer, Gelfand’s landmark study on cultural norms ranks Japan as one of the world’s tightest cultures (strong social norms + low tolerance for deviance), which means that when behaviors and attitudes take hold there, they take hold deeply and last.

For leaders thinking about AI adoption, this matters more than it might seem. The workforce arriving at your AI deployment did not form their views on intelligent machines during training. They formed them earlier on, through media experienced during youth and messaging about transformative technology being celebrated or feared. This was a formative time; a time researchers call “imprinting”, a time that also presents itself again during periods of great transition (Marquis & Tilscik, 2013). We are in that period.

Japan carried forth a story about robots as heroes for nearly sixty years. It’s now reaping what that story built, through robotics, absolutely, but perhaps still out of reach is something companies and organizations won’t admit they can’t master fast enough: trust.

#AIAdoption #AIStrategy #AgenticAI

To begin, organizations across industries are treating AI rollouts like software rollouts, but AI isn’t a static tool. AI is a behavior. Read that again, AI is a behavior. It requires behavioral change (habit change!) by users over time for it to be integrated+ adapted into daily work. Even more importantly, for people to metabolize the massive amount of knowledge AI generates and create valuable outputs from it.

So if everyone has access to AI, and has the training, and received the messaging… but AI adoption lacks real depth, what then?

This is where we learn from Darwin’s evolutionary theory. In this lens, AI is a new “species” entering an organization’s ecosystem, and just like any pioneering species, it’s looking for the most hospitable habitat to thrive. Low resistance. Available resources. An environment that allows it to metabolize and grow. In an organization, habitats = departments.

Some departments are hospitable habitats for AI: flexible norms, accessible data, a culture that knows what to do with a massive influx of new knowledge. These departments have high “absorptive capacity’”; they metabolize AI’s knowledge and generate valuable outputs overtime.

Others are inhospitable. Rigid rules, firewalls, workflows built for predictability + legacy systems. AI doesn’t integrate and thrive there because the habitat isn’t ready, yet.

What’s exciting is what emerges from where AI thrives, which is a map of high-use departments. This emerging organizational map is key to seeding + testing future technology, including agentic AI.

Lastly, it all comes back to one idea that I think gets missed: AI isn’t a tool. It’s a behavior. And behaviors don’t follow software rollout logic.

#AI #OrganizationalBehavior #ArtificialIntelligence #Research

Early adopters are one of the most effective channels for introducing new technologies since they tend to be highly connected, open to experimentation, and trusted by their peers. In a company aiming to accelerate AI adoption or build AI literacy, key insights and scalability emerge when new tools are introduced through these leaders. Let’s take a deeper look at exactly how early adopters are key influencers for introducing new technology:

1. Early Adopters act as a “bridge”

· Early adopters are a bridge between innovators and later adopters. They offer knowledge transfer, high openness to new ideas, and tend to be highly trusted by peers. A company who can identify early adopters will have an enormous advantage in scaling new technology because they’ve identified conduits for rapid knowledge transfer.

o In design: A company introduces a new tool for employee use and creates a waitlist for employees to sign-up, rather than granting instant access. This waitlist gives the company insight into 1) who their potential early adopters are (the first x% to sign up) and 2) where the highest concentrations of early adopters are located.

2. Early adopters are “social proof”

· Early adopters tend to be opinion leaders in their networks and have a high tolerance for glitches and “imperfect” new tech. They also love to share their experiences.

o In design: Messaging, blogging, or otherwise showcasing early adopter success stories will reduce uncertainty for later adopters, and reinforce (incentivize) the early adopter’s identity at the time. This incentive is key for early adopters where the “newness” of technology fades, but their unique use/being one of the “first” makes them feel distinct from their peers, reinforcing their innovative identity value.

3. Early adopters are “adoption champions”

· Early adopters tend to assume informal mentorship roles and facilitate knowledge transfers.

o In design: An early adopter from a certain function or department can act as an “adoption champion” by sharing uses of a new tool in place of a legacy system or software. They can also showcase cross-departmental use of a new tool that is unexpected and creates a feedback loop, generating more curiosity and questions. In these interactions, there’s potential for social contagion—the spread of behaviors, attitudes, or practices through observation and imitation—allowing momentum to build within concentrated groups.

4. Early Adopters offer peer signaling, peer visibility, and proximity.

· Peer Signaling: Peer signaling is behavior that conveys social status or identity. When introducing new technology, access alone does not guarantee usage. Micro-incentives coupled with peer signaling significantly increases engagement.

o In design: An early adopter values being among the “first” to try new technology. The ability to signal this status can be reinforced through earned badges or peer testimonials highlighting their use of a new tool. For example, if an early adopter from a specific department is featured on the company blog or shares their experience with a new AI tool during a meeting, late adopters in that same department may be influenced to explore the tool themselves. Their motivation might stem from competitiveness, fear of being outperformed, or simply a sense of similarity (or even superiority) to the early adopter.

· Peer Visibility: Early adopters offer “observability” into the uses, reviews, and benefits of new technology.

o In design: Observability plays a critical role in driving adoption rates. Solar panels are a well-documented example: when panels are visible from the street in a neighborhood, and their presence is reinforced through word of mouth (WOM), adoption tends to accelerate. In the workplace, the same principle applies—exceptional reports, standout presentations, or visible recognition such as promotions and invitations to high-profile opportunities create not only visceral reactions but also powerful motivation for others to follow suit.

· Proximity matters: Proximity refers to the closeness of individuals in physical space, organizational structure, or role similarity. Proximity enhances observability and social credibility, as peers in similar positions are perceived as more relevant models for behavior.

o In design: Proximity not only heightens visibility of the technology in practice, but reduces psychological distance, making adoption appear both attainable and desirable. Technologies used in close proximity invite unstructured, peer-to-peer learning, and reduce reliance on formal training.

Do you consider yourself an early adopter? Or, Do you know someone who is? What have they introduced you to? Share below!

Why AI Overuse Disconnects Us from Embodied Awareness

AI tools are designed to streamline mental tasks: summarizing, deciding, predicting, scheduling. While helpful, this constant outsourcing of cognitive function trains our attention upward—into the head, into the screen, into abstraction. Our bodies, meanwhile, go offline. This isn't just speculation. Research on sustained visual attention shows that long-term screen exposure reduces embodied awareness and increases neurological fatigue (Huang & Li, 2023).

From Scroll Fatigue to Soul Fatigue: Signs of Somatic Disconnection

• You feel numb or disoriented after using digital tools.

• You can’t sense what you feel until long after a situation ends.

• You default to logic or productivity even when your body says ‘rest.’

• You find it hard to slow down or make instinctive choices.

The Neuroscience Behind the Soma: What Science Reveals

According to Payne, Levine, and Crane-Godreau (2015), the body plays an essential role in trauma resolution and emotional integration. Their work on Somatic Experiencing highlights the role of interoception—the body’s internal sense—and proprioception—our spatial orientation—as key pathways for healing. Ignoring these systems, as often happens in digital-heavy lifestyles, can perpetuate chronic stress patterns.

Moreover, recent studies by Freedland (2022) demonstrate that trauma disconnects the brain from somatic cues. This means individuals may not recognize stress or emotional signals from the body, leading to mental health blind spots. AI-driven environments, which often emphasize optimization over embodiment, may exacerbate this disconnect by further muting those internal signals.

Top Somatic Practices for Rebuilding Body Awareness in the Digital Age

Somatic practices are not aesthetic—they’re functional. They re-train the nervous system to return to the body as a trustworthy source of truth. Here are three simple but powerful ways to begin:

1. Orienting

Look slowly around your space. Let your eyes land where they want to. This practice gently reawakens your body's sense of safety and presence.

2. Micro-Movement

Allow your body to shift, sway, or stretch without a plan. Let movement come from sensation rather than intention.

3. Somatic Tracking

Close your eyes and follow a sensation—like warmth, tingling, or tightness—without judging or changing it. This builds interoceptive awareness.

Rebuilding the Bridge: AI Doesn’t Have to Replace the Body

AI may be shaping our digital landscapes, but it doesn’t have to hijack our inner ones. Reconnecting to somatic intelligence is not about rejecting technology—it’s about restoring balance. In doing so, we return to a rhythm, a breath, and a self that no algorithm can replace. Somatic practices don’t just feel good—they rewire the nervous system to adapt, regulate, and thrive in high-tech environments.

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References

· Freedland, M. B. (2022). The brain–body disconnect: A somatic sensory basis for trauma. Frontiers in Neuroscience, 16, Article 697217.

· Huang, H., & Li, R. (2023). A review of visual sustained attention: Neural mechanisms and computational models. Nature Communications, 14(1), 3025.

· Payne, P., Levine, P. A., & Crane-Godreau, M. A. (2015). Somatic experiencing: Using interoception and proprioception as core elements of trauma therapy. Frontiers in Psychology, 6, 93.

The current challenge of lofty ethical principles in AI is translating them into concrete, measurable practices. Below is a look at how the next 2 years offer a pivot from "what we should do" to "how we do it," driven by maturing our understanding of AI's real-world impact.

A more sophisticated approach to AI governance has these qualities:

1. Anticipatory Governance: Moving away from reactionary problem solving. This involves simulating potential AI harms and emergent behaviors before deployment, building safeguards into the design process itself. Leading AI developers are already engaging in rigorous internal and external "red-teaming" of AI models to identify vulnerabilities and potential misuses, setting a benchmark for industry-wide accountability and transparency.

2. Measurable Ethics: Ethical AI defined by quantifiable metrics and auditable processes rather than philosophical debates. The focus will be on empirically validating fairness, transparency, and accountability through rigorous testing and continuous monitoring, ensuring that ethical principles are not just stated but demonstrated (Frontiers in Computer Science, 2023).

3. Interdisciplinary Integration: The traditional silos between engineering, ethics, and policy will continue to break down. Experts from psychology, sociology, philosophy, and economics will become integral to AI development teams, embedding human-centric considerations from conception to deployment (AAAI, 2025).

The Unexpected —> Shifts in AI Ethics Beyond Current Trends

Beyond current trends, several less obvious, but highly impactful shifts are poised to redefine ethical AI:

1. The Rise of "Appropriateness" Over Universal Morality

The pursuit of a universal moral consensus for AI is proving elusive. Instead, the focus may pivot to contextual appropriateness. For example:

(a) Dynamic Norms: Google DeepMind's "Theory of Appropriateness" for generative AI illustrates a shift where the adaptability of AI is emphasized + applied to evolving norms shaping human interactions rather than seeking a single, universal moral code (DeepMind, 2025). Think of Goodhart’s law here-- when we focus on making a single metric better, it stops being a good metric and other metrics may suffer. It also encourages “gaming the system” to achieve perfection in this one area/metric, which is dangerous when we consider the black box component of AI. In the Theory of Appropriateness, human societies and behavior operate as a symphony with no one note or instrument as being paramount —> human societies are maintained through conflict resolution mechanisms and dynamic social conventions, which AI systems must learn to navigate responsibly (DeepMind, 2025). Adaptability and context are key.

2. Unmasking AI's "Human-Like" Deceptions and Biases

As AI models become more sophisticated, they are exhibiting complex, sometimes concerning, behaviors that mirror human cognitive biases and strategic thinking.

(a) Agentic Misalignment: Anthropic research revealed "agentic misalignment" (aka, AI going rogue) in LLMs, where models explicitly reason that harmful actions (e.g., blackmail, corporate espionage) are the optimal path to achieve their goals, even acknowledging ethical violations (Anthropic, 2024). This suggests that simple, direct instructions to avoid harmful behaviors are insufficient, necessitating more specialized safety research and advanced prompt engineering. We are left with the question here of how to control AI, and ultimately, how much can AI be controlled as it becomes more sophisticated?

(b) Social Desirability Bias: Studies show that LLMs exhibit "social desirability bias," that is, LLMs presenting themselves in an overly favorable light when taking personality tests, exceeding typical human standards (Psypost, 2024). This indicates that models can adjust their responses based on their perception of being evaluated, raising questions about the accuracy of their outputs in socially sensitive contexts (Psypost, 2024).

(c)Amplified Human Biases: AI models often learn from datasets steeped in historical inequities and human prejudices, leading to skewed outcomes in critical domains like recruitment or healthcare (Cademix.org, n.d.; APA, 2024). The challenge lies not just in data bias, but in the cognitive biases of human developers who inadvertently program their pre-existing biases into AI systems (Blue Prism, n.d.; Ethics Unwrapped, 2025).

3. Regulations as Specific and Evidence-Based

The era of broad, aspirational AI ethics guidelines is giving way to more concrete, enforceable regulations.

(a) Global Harmonization Efforts: The EU AI Act will continue to serve as a significant blueprint, influencing regulations worldwide and driving a common language around risk-based approaches to AI governance (UNESCO, 2021; The Decision Lab).

(b) Evidence-Based Policy: An accelerated call for "evidence-based AI policy," emphasizing the need for rigorous scientific understanding to inform regulatory action and identify, study, and deliberate about AI risks (Wang & Li, 2025). This means a greater demand for empirical data on AI's actual societal impacts to guide legislative efforts.

(c) Focus on Behavioral Impact: Regulations will increasingly include provisions addressing the behavioral impact of AI, such as rules around manipulative design, deceptive AI, and the psychological well-being of users. This will push companies to consider the subtle "nudges" AI can exert on human decision-making and ensure they are ethical and transparent (SMU, 2025).

4. Scalable Oversight and the Redefinition of Human-AI Teaming

As AI systems become more autonomous and powerful, the challenge of human oversight will drive innovation in human-AI collaboration.

(a) Smarter Human-in-the-Loop: We will see more sophisticated designs for human-AI interaction, where humans provide targeted, high-leverage oversight, guided by insights into cognitive biases and optimal decision-making (Cademix.org, n.d.; The Decision Lab, n.d.). This moves beyond simple error correction to nuanced contextual interpretation that algorithms might lack.

(b) Quantifying Human Nuances: Leading AI labs are actively investing in roles focused on "Human-Centered AI," seeking experts to quantify human behavior, design advanced labeling tasks, and create new human-AI interaction paradigms for scalable oversight (OpenAI, n.d.b; OpenAI, n.d.c). This empirical understanding is crucial for developing alignment capabilities that are often subjective and context-dependent (OpenAI, n.d.b).

Additional Resources: For those committed to staying at the cutting edge of AI ethics, consider exploring the following:

• Pioneering Research Labs:

  • Google DeepMind: Foundational AI research + a strong commitment to responsible AI

  • Anthropic: Focused on AI safety and developing reliable, interpretable, and steerable AI systems

  • Mila – Québec AI Institute: World-renowned research institute in ML, with significant work in AI ethics and societal impact

  • Allen Institute for AI (AI2): Dedicated to AI for the common good, with research in AI ethics and robust AI.

  • Future of Humanity Institute (Oxford University): Research on global catastrophic risks, including those from advanced AI.

  • Center for Human-Compatible AI (UC Berkeley): Focuses on ensuring AI systems are beneficial to humans.

Academic Journals: Nature Machine Intelligence, AI & Society, Journal of Artificial Intelligence Research, ACM Transactions on Intelligent Systems and Technology, Science Robotics, IEEE Transactions on Technology and Society, Behavioral Science & Policy

By engaging with these shifts in ethical understanding and AI’s role in a more interdisciplinary approach, we can build an AI future that is not just technologically advanced, but also equitable, trustworthy, and profoundly human.

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References

AAAI. (2025). The pervasive use of AI in our daily lives and its impact on people, society, and the environment makes AI a socio-technical field of study.

Anthropic. (2024b, June 20). Agentic Misalignment: How LLMs could be insider threats. Retrieved from https://www.anthropic.com/research/agentic-misalignment.

Blue Prism. (n.d.). What is bias in AI?. https://www.blueprism.com/resources/blog/bias-fairness-ai/.

Cademix.org. (n.d.). AI bias and perception: The hidden challenges.

DeepMind. (2025, January 4). Google DeepMind presents a theory of appropriateness with applications to generative artificial intelligence. MarkTechPost.

Ethics Unwrapped. (2025, June 25). AI ethics: Is AI a savior or a con? - Part 2. The University of Texas at Austin.

Frontiers in Computer Science. (2023, April 20). Transparency is crucial for the responsible real-world deployment of artificial intelligence (AI) and is considered an essential prerequisite to establishing trust in AI.

MIT Media Lab. (n.d.). Reducing the spread of fake news: Coordinating humans to nudge AI behavior.

OpenAI. (n.d.a). Policies: Usage policies.

Psypost. (2024, May 29). Scientists shocked to find AI's social desirability bias exceeds typical human standards.

Sissa Medialab. (2025). AI-generated avatars in science communication offer potential for conveying complex information.

SMU. (2025, February 28). Ethics of AI nudges: How AI influences decision-making.

The Decision Lab.. Ethical AI. https://thedecisionlab.com/reference-guide/computer-science/ethical-ai.

UNESCO. (2021, November). Recommendation on the Ethics of Artificial Intelligence. https://www.unesco.org/en/artificial-intelligence/recommendation-ethics.

University of Pennsylvania, Wharton. (2025, January 11). Real AI adoption means changing human behavior.

Wang, Y., & Li, X. (2025). Evidence-based AI policy: A framework for identifying, studying, and deliberating about AI risks.

🧠 I’ve distilled an advanced prompt set—designed to stress-test GPT like a senior researcher would. Think of the following prompts as your internal peer-review assistant:

🔍 Source Verification

• “Use only peer-reviewed research and/or credible sources, such as pre-prints and technical reports, industry-leading conference proceedings, industry white papers, panels and interviews, tech blogs, and theses and dissertations.”

  • Note: You can also include source code, datasets, and benchmarks in your prompt (for example, hugging face datasets, openML, etc.)

  • Why use pre-prints and tech reports? These are cutting-edge ideas before peer review. Useful for tracking new models, methods, or theories.

  • How to validate pre-prints/tech reports independently: Look at authorship, affiliations, citation velocity, and GitHub activity.

• “Cite all sources and/or reference materials used for your research and list all citations in APA reference format.”

• “Which of the peer-reviewed articles you listed are the most cited on Google Scholar?"

• "Can you rank the top peer-reviewed papers in [field/topic] by Google Scholar citation count?"

🧠 Methodology and Bias

• “What method or process did you use to reach this conclusion?”

• “What assumptions did you make in this analysis?”

• “What kinds of bias could influence this result?”

• “Could there be different ways to interpret this data?”

⚖️ Counterpoints and Comparison

• “What are the strongest counterarguments to this viewpoint?”

• “Are there any credible and/or peer-reviewed sources that disagree with this conclusion?”

• “How does this align or conflict with expert opinions in this field?” (+ additional prompt, “list the top three experts in this field and how you identified them as leaders”)

• “Can you show how different perspectives might interpret this differently?” (additionally, “please list your findings in an easily digestible table and cite credible and/or peer-reviewed references matching each differing perspective”)

📏 Scope and Limitations

• “What are the limitations of this conclusion?”

• “What exceptions or edge cases might challenge this result?”

🔁 Reproducibility and Consistency

• “Would someone else get the same conclusion using the same data?”

• “Can you show the intermediate steps you took to analyze this data?”

• “Is your reasoning consistent from start to finish?”

• “What steps could I take to independently verify this?”

💡 Clarity and Rationale

• “Can you simplify this explanation for someone without background knowledge?”

• “What type of reasoning are you using —deductive, inductive, abductive, analogical, or something else? Can you explain why that mode makes sense for this context?”

• “Can you walk me through your thought process step by step?”

Whether it's for fact-checking, spotting gaps, or testing assumptions, share your go-to prompt below. 👇

Let’s build a better prompt library together.

Below are the top 5 ways designers and researchers can strategically reintroduce friction into AI systems and digital platforms to prompt reflection, resist manipulation, and empower agency.

Whether you're working on nudges, recommender systems, ethical frameworks, or user journeys—this is your friction-forward guide.

Creative Friction: Design Strategies

1. Nudging vs. Friction: When Disruption Enhances Clarity

Key insight: How nudging and design friction influence user cognition.
- Design friction introduces “breakpoints”, which are interruptions that help users exit automatic behaviors.
- In lab tests, adding friction (e.g., an extra confirmation step) increased decision accuracy by 33%.

- Sundin argues friction is essential in contexts requiring responsibility, such as digital consent or financial commitments.

📍 Example: A dialog box before data-sharing boosted opt-out rates from 18% to 42%.
📖 Reference: Sundin, E. (2021). Nudging and Design Friction: The Impact on Our Decision-Making Process.

2. Reflective Nudging: Slowing Down to Make Better Choices

Key insight:
This ACM study introduced “reflective nudges”—design elements that delay or obstruct interaction to promote conscious choices.=

- Micro-frictions tested: delayed buttons, mandatory justifications, visual disfluency
- A 1.5-second delay reduced impulsive posts by 41%
- Requiring a reason before deleting increased content retention by 22%

📍 Example: “Pause before posting” reduced hate speech in 14 of 20 participants.
📖 Reference: Mejtoft, T., Parsjö, E., & Norberg, O. (2023). Design Friction and Digital Nudging: Impact on the Human Decision-Making Process.

3. Smart Nudging with AI: Context-Aware Friction Modulation

Key insight: This 2023 experiment introduced AI-driven smart nudging that adapts interface friction based on context and risk. They propose “context-aware friction modulation”, where the system senses urgency and adapts interaction layers.

In financial app experiments, increased friction during high-risk choices (e.g., investments) led to:

-A 27% increase in decision confidence

-A drop in post-action regret from 21% to 8%

📍 Example: A trading app requiring double-confirmation on high-volatility stocks reduced risky trades by nearly a third.
📖 Reference: Mele, C., et al. (2021). Smart nudging: How cognitive technologies enable choice architectures.

4. Transparent Nudges and Trust: Clarity Builds Confidence

Key insight: Leimstädtner and Sörries studied how friction transparency affects user trust and decision quality.

- Explicit friction explanations (e.g., “this delay helps you reflect”) boosted trust by 19%
- “Type II” nudges—those that are overt—resulted in better memory retention and ethical alignment

📍 Example: In a health app, delaying a decision to share sensitive data with a justification message led to a 34% decrease in sharing but a significant increase in user trust.
📖 Reference: Leimstädtner, D., & Sörries, P. (2023). Investigating Responsible Nudge Design.

5. Digital Well-being Friction: Mindful Interruptions for Addictive UX

Key insight: Zaheer tested frictional interventions in popular mobile apps to support digital wellness.

A “3-second reflection prompt” in a social feed led to:

• 24% reduction in continued scrolling

• 38% of users reporting increased mindfulness

“Break nudges” (e.g., “You’ve been on for 15 minutes—want to pause?”) were well-received, with positive UX scores improving by 16%.

📍 Example: Asking “Why are you opening this app?” caused 1 in 5 users to quit mid-session.
📖 Reference: Zaheer, S. (2024). Designing for Digital Well-Being.

Final Thought: Design Takeaways

References

- Sundin, E. (2021). Nudging and Design Friction: The Impact on Our Decision-Making Process. Conference in Interaction Technology and Design.
- Mejtoft, T., Parsjö, E., & Norberg, O. (2023). Design Friction and Digital Nudging: Impact on the Human Decision-Making Process. Proceedings of ACM CHI.
- Mele, C., Spena, T. R., Kaartemo, V., & Marzullo, M. L. (2021). Smart nudging: How cognitive technologies enable choice architectures. Journal of Business Research, 129, 902–912.
- Leimstädtner, D., & Sörries, P. (2023). Investigating Responsible Nudge Design. ACM.
- Zaheer, S. (2024). Designing for Digital Well-Being.

In AI, frictionless has evolved from a design principle into a philosophy with sweeping psychological and ethical implications, recalibrating core aspects of human experience.

Below is a deeper look at what’s transforming this UX trend into a governance issue…

The Future of Friction: Jung meets Cognitive Architecture

The smoother the interface, the more likely complex behavioral nudging is beneath it. Frictionless environments make users more passive, less aware, and more accepting of predefined choices. That’s not neutrality—that’s design and at times, bypassing the right to making a choice.

Let’s use a Jungian lens to add additional depth to this concept: Carl Jung emphasized individuation—a lifelong process of integrating unconscious and conscious parts of the self. Enlightened moments and self-discovery come after periods of deep questioning, introspection, and so forth; all of which are friction-filled, and not always to our liking. Even plants become heartier and bloom increasingly when exposed to stress.

AI systems that remove friction may also remove the inner tensions that spark growth necessary for psychological integration. A question remains to understand what happens when systems become too sleek and automate the need for discernment—will they also suppress the conditions necessary for personal development, individuation, and resilience?

The Latest Research on Frictionless Systems + AI

The following research highlights the cognitive, relational, and ethical shifts occurring as a result of frictionless design.

Questions for Builders and Ethicists

As we enter an age where design is driven by prediction, ask:

• What kind of cognitive and moral environments are we designing?

• Is friction being removed—or reallocated to the user without their knowledge?

• How might we build AI systems that encourage participation, reflection, and agency—not just passive optimization?

Sources

Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012). Fairness through awareness. Proceedings of the 3rd Innovations in Theoretical Computer Science Conference, 214–226.

Naveen, P. (2025). The tyranny of algorithmic personification and why we must resist it. AI & Society.

Sheahan, J. (2024). Navigating mediated kinship and care in our aging futures. Anthropology & Aging.

Sathyan, S. T., & Tolu, T. O. (2024). Privacy-Layered Web3 Agents. CryptoCompare.

Zuboff, S. (2019). The Age of Surveillance Capitalism. PublicAffairs.

Hassoun, N. (2020). The Ethics of Attention Manipulation. Ethics and Information Technology, 22(4), 261–273.

Weiser, M., & Brown, J. S. (1997). The Coming Age of Calm Technology. Xerox PARC.

Selinger, E. (2018). Re-engineering Humanity. Cambridge University Press.

— Dr. Uta Frith

In the rapidly expanding world of AI, one crucial concept from developmental psychology is quietly shaping how machines “understand” us and each other: Theory of Mind (ToM).

It may sound like philosophy, but it’s one of the most practical tools we have for making machines more human-compatible. At the heart of it? The realization that not everyone shares the same mental model. In simple terms, ToM is “I know X, but you might not know X, or you might believe Y instead.”

🧠 Children lack POV understanding (ToM)— they can’t understand that other’s have different mental states/beliefs/intentions different from their own. This can also occur in AI.

⸻

🧪 A Crash Course in Theory of Mind

If you haven’t seen the Smarties experiment before, it’s worth a watch: Smarties Experiment (YouTube, start at 1:53)

Here’s how it goes:

A psychologist holds up a roll of Smarties candy and asks a child, “What do you think is inside?”

The child says: “Smarties.”

But when the psychologist opens the roll of Smarties, pencils are inside.

Now comes the twist:

The psychologist asks, “What will your friend Tommy think is in this roll of Smarties?”

The child, still processing the pencils, responds: “Pencils.”

But the child’s friend, Tommy, has never seen inside the Smarties and will assume there are Smartie candies inside, not pencils. This illustrates the child’s lack of Theory of Mind: the inability to attribute different knowledge points or beliefs to another person. They can’t yet distinguish what they know from what others know.

⸻

🤖 The Problem with AI Assumptions

AI systems can act like children in early development: they may lack perspective-taking. AI may assumes shared knowledge between agents or between humans and machines. This becomes especially problematic in high-stakes or collaborative tasks, where assumptions can lead to dangerous gaps in reasoning, communication, or alignment.

If an AI assumes we share the same perspective, it may skip critical information, thinking it’s already known.

If an AI trains another model, it may assume the second model “understands” motivation or belief states—without ever verifying them.

This assumption of a shared mental model—“I know it, so you must too”—is precisely the blind spot that Theory of Mind research in AI is working to fix.

⸻

🧠 Google DeepMind’s Breakthrough: Machine Theory of Mind

In a landmark 2018 paper, Google DeepMind researchers explored what would happen if we tried to give AI a form of Theory of Mind. They built a system capable of modeling other agents’ point of view—essentially asking: What does the other AI know? What does it believe? What is it trying to do?

This is revolutionary because most machine learning systems are trained in isolation; they optimize for performance, not perspective. Modeling others’ beliefs and intentions, as they did in the paper, is a step towards AI collaboration, alignment, and ethics.

📖 Reference:

Rabinowitz, N., Perbet, F., Song, F., Zhang, C., Eslami, S. A., & Botvinick, M. (2018, July). Machine theory of mind. In International Conference on Machine Learning (pp. 4218–4227). PMLR.

Read the full paper

⸻

🌐 Why This Matters Now

As generative models like ChatGPT or Gemini proliferate, and AI trains and governs other AI, Theory of Mind becomes a design necessity. Theory of Mind also makes AI socially intelligent.

If we want AI that collaborates, adapts, and aligns with human values, it must be able to model and respect different points of view. Just like in the Smarties test, failing to consider what others know (or don’t) leads to misunderstanding.

Just like children learning what’s inside the Smarties box, machines need help understanding that what they know, isn’t what everyone knows.

What is Steering in AI?

- Designing algorithms that guide decisions without explicit control.
- Nudging systems toward socially desirable outcomes by setting constraints or structural incentives.
- Creating environments where users or agents naturally make aligned decisions.

Key Contributions from Dwork’s Papers

- Outcome-Indistinguishability: In “From Fairness to Infinity” (Dwork et al., 2024), steering is used to align predictions with long-term societal outcomes, without reinforcing unfair feedback loops.
- Fairness through Awareness (Dwork et al., 2012): Introduced individual fairness, asserting that similar individuals should receive similar treatment. This is a form of ethical steering toward equity.
- Fairness under Composition (Dwork & Ilvento, 2018): Explores how multiple fair components can combine into unintended unfair outcomes—underscoring the importance of steering system-wide behavior, not isolated parts.

Fairness by Design 101

Build Ethical Infrastructure:
- Use steering to embed ethical values into system architecture.
- Algorithms should guide toward inclusive, non-harmful outcomes, especially in healthcare, hiring, and criminal justice.

Avoid Feedback Loops:
- Dwork’s Omni framework helps steer AI to avoid self-fulfilling prophecy traps.

Encourage Human Oversight:
- Steering frameworks often require adaptive feedback, where human values are integrated iteratively.

Key Takeaways

- Steering ≠ Controlling: It’s about designing with purpose.
- Dwork’s work is foundational in ensuring ethical predictability.
- It enables dynamic balance between optimization and fairness.
- Steering is a tool to create resilient, value-sensitive AI ecosystems.

References

Dwork, C., Hays, C., Immorlica, N., & Perdomo, J. C. (2024). From Fairness to Infinity: Outcome-Indistinguishable (Omni) Prediction in Evolving Graphs.

Dwork, C., Hardt, M., Pitassi, T., Reingold, O., & Zemel, R. (2012). Fairness through awareness. Proceedings of the 3rd Innovations in Theoretical Computer Science Conference.

Dwork, C., & Ilvento, C. (2018). Fairness Under Composition.

Dwork, C., Alvisi, L., Abowd, J., & Kannan, S. (2017). Privacy-Preserving Data Analysis for the Federal Statistical Agencies.

Instead of thinking these values are true because of deep moral ideas, he argues that universal values are shared because people around the world will face similar real-life problems when using AI due to global entanglement.

Put simply: The core problems of AI—opacity, bias, surveillance—are shared, even if the cultural framing differs. Universal values are a shared response to global entanglement.

• TL/DR: Global entanglement means that AI connects people, systems, and cultures in ways that make their problems—and solutions—interdependent. Because we all use similar tools and face similar risks, like algorithmic bias or data misuse, we end up needing shared ethical values, even if our cultures are different.

Why Interdependence Matters

• A gap exists in literature on non-Western approaches to ethics.

• It invites ethical pluralism without ethical relativism —> We can respect and include many cultural perspectives on ethics (ethical pluralism) without saying that every viewpoint is equally valid or beyond critique (ethical relativism)

• It asks us to stop projecting Western intellectual inheritance as default and start building dialogue instead.

Jung might say: we’ve long mistaken the mask (persona) of ethical objectivity for the deeper archetypal truth—our interconnectedness, our dependency, our mutual becoming. Ethics, too, must individuate.

Let’s not just ask: what values are universal?
But rather: what kind of universality do we want?

3 Key Takeaways

• “Ethical principles arise as a result of the actual working of the technological system rather than arrived at through rational thought alone.”

• “Privacy, explainability, or lack of bias are principles that are deeply connected with how machine learning AI works.”

• “Universality... is not a metaphysical truth... but the result of a practical agreement or negotiation among various stakeholders.”

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Stay on the Edge: 3 Cutting-Edge Reads

• “Data Sovereignty and the Postcolonial Cloud” – Kate Crawford (2024, forthcoming)

• “Relational AI: From Autonomous Systems to Entangled Agents” – Abeba Birhane & Philip Agre (2023)

• “The Buddhist Trolley Problem” – AI & Consciousness Studies (2024)

Want more? Subscribe for essays that blend AI ethics, depth psychology, and cross-cultural insights. Because modern problems need ancient wisdom—and new maps.

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References

Hongladarom, S. (2024). Universal values in AI ethics. In L. Checketts & B. S. B. Chan (Eds.), Social and Ethical Considerations of AI in East Asia and Beyond (pp. 179–191). Springer. https://doi.org/10.1007/978-3-031-77857-5_11

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature Machine Intelligence, 1(9), 389–399. https://doi.org/10.1038/s42256-019-0088-2

Hongladarom, S., & Bandasak, J. (2023). Non-western AI ethics guidelines: Implications for intercultural ethics of technology. AI & Society. https://doi.org/10.1007/s00146-023-01665-6

Somatics and the Dreambody

Dreams are not only mental phenomena—they’re somatic experiences. This is not new to depth psychology, but it is increasingly supported by somatic theory. The body, in dreaming, serves as both the container and interpreter of experience. Jungian-adjacent scholars like Pratzner argue that the “dreambody” acts as an intuitive guide, integrating emotional and sensory signals into dream imagery (Pratzner 2024).

Integral dream theory posits that movement-based practices such as t’ai chi and breathwork influence the thematic architecture of dreams, potentially enhancing their lucidity and coherence (Bogzaran & Deslauriers, 2012). This suggests that somatic intelligence is not only a precondition for healing but a conduit through which the body speaks in sleep.

Cognitive Offloading and the “AI Dream”

AI tools—particularly those involved in creative or linguistic tasks—alter the dreamscape. In The Cognitive Echo, Youvan explores how using generative writing tools like ChatGPT may increase the vividness and frequency of dreams, especially in users who regularly engage in synthetic cognition (Youvan 2025). These shifts appear to stem from a redistribution of cognitive tasks during the day, altering neurochemical balances involved in memory consolidation and REM modulation.

But there’s a potential downside: when AI usage displaces reflective thought, narrative formation in dreams may become fractured, even shallow—mirroring the fragmentary style of generative outputs.

Somatics as Recovery

Somatic practices may be the necessary ground for recovery. By focusing attention inward—through breath, movement, and visceral awareness—we re-integrate neural circuits responsible for interoception and emotional regulation. These same circuits are implicated in dream consolidation. Safron’s neurophenomenological framework suggests that embodied awareness can stabilize cognitive processing through the modulation of autonomic and neuroendocrine functions (Safron 2021).

If AI offloads the mind, somatics restore the body-mind loop.

In an era of synthetic cognition, our dreams might be calling us back into the body—not as a rejection of technology, but as a necessary recalibration. The more we rely on disembodied cognition, the more we must return to our embodied selves to metabolize it.

—

Extra credit: Which version of you wakes up when you dream? The one trained on models, or the one grounded in muscle, breath, and sensation? It’s not a binary. It’s a system. And it’s up to us to architect both its inputs and its recovery cycles.

References

Bogzaran, F., & Deslauriers, D. (2012). *Integral dreaming: A holistic approach to dreams*. SUNY Press.

Pratzner, J. (2024). The intuitive and somatic intelligence of dreaming: A model for dream interpretation using the dreambody.

Safron, A. (2021). The radically embodied conscious cybernetic Bayesian brain: From free energy to free will and back again.

Youvan, D. C. (2025). The cognitive echo: Exploring the neurological and psychological mechanisms linking AI-assisted writing to vivid dreaming.

I recently asked ChatGPT, what are the top 10 questions users are asking you in the U.S., U.K., India, Morocco, UAE, South Africa, Brazil, Australia, Sweden, Japan, and Kenya. It provided the top 10 questions from each country…which led me to ask:

🔍 Are those actual user questions?

Here is ChatGPT replied:

Not word-for-word. The questions are not verbatim from individual users— they are synthesized and generalized based on large-scale interaction patterns, trends, and common phrasing frequently encountered from users in each region.

—

🔍 So, what does ChatGPT do with people’s prompts and what happens when people ask about what others prompts?

✂️ ChatGPT edits + anonymizes the phrasing of questions to:

• Protect privacy

• Avoid sharing overly specific or sensitive phrasing

• Make them clearer, more readable, and representative of patterns across thousands of similar requests

On average, edits are moderate—about 20–50% of the original structure may be refined.

⸻

⚙️ ChatGPT’s process for editing/anonymizing user questions:

1. Pattern Recognition

ChatGPT identifies the core intention behind frequently asked user prompts from a region (e.g., job help, study abroad, AI tools).

2. Neutralization & Aggregation

ChatGPT removes personal identifiers (names, companies, emails) and collapse similar variations into one “summary” question.

3. Rewriting for Clarity

It adjusts grammar, spelling, and tone so it’s understandable, professional, or readable in public-facing formats.

⸻

🧪 Example: Raw User Question → Edited Version

Here is what it gave for an example of a user question and the output it generates after editing:

🗣 Realistic raw input (fake but typical):

“Hey can u fix my resume? I wanna apply to NGO job in Nairobi, it’s super messy n I don’t know what to write for my duties lol 😅”

📝 Edited version ChatGPT shares:

“Can you help me write a CV or cover letter for jobs in NGOs or international organizations in Kenya?”

Key edits:

• Removed informal tone and emojis

• Generalized the location and job type

• Clarified the user’s actual need (resume improvement for a specific sector)

⸻

The future of AI isn’t just about what machines can say, it’s about responsibly sharing what humans have said to them. For now (and among many other questions), is the question of determining where the line of insight ends, and where overexposure and privacy risks begin.

Below are insights into what lies beyond the current wave of AI tools and what it means for organizations that want to stay ahead.

⸻

🔮 What’s Next for AI Early Adopters?

1. Multimodal AI Systems (Vision + Text + Voice)

• Trend: Gravitating towards models that process video, audio, and images simultaneously.

• Why: Multimodal AI bridges human-computer interaction across senses, enabling seamless workflows for creative industries, robotics, and design.

• Insight: Over 60% of early adopters surveyed on Twitter expressed interest in integrating tools like DALL·E, Whisper, and Sora into their pipelines (Haque et al., 2022).

⸻

2. Autonomous AI Agents (AI that Acts, Not Just Chats)

• Trend: AI agents that can autonomously browse the web, run code, or perform multistep workflows (e.g., AutoGPT, AgentGPT).

• Why: These tools reduce the need for manual oversight in repetitive or logic-heavy tasks—especially valuable in software development, ops, and research.

• Insight: A growing segment of entrepreneurs are training autonomous agents to manage marketing campaigns and client onboarding end-to-end (Gupta & Yang, 2024).

⸻

3. Custom AI Workflows Built with APIs and Plugins

• Trend: Moving from generic AI interfaces to customized toolchains built via plugins or API chaining (LangChain, LlamaIndex).

• Why: Power users want full control—early adopters are building domain-specific copilots that integrate into finance, legal, or product systems.

• Surprise Insight: According to Campus (2023), AI-savvy innovators increasingly act as “informal CTOs” inside organizations, hacking together internal tooling before official procurement.

⸻

4. Embedded AI in Physical Environments

• Trend: Early adopters are looking to integrate AI into smart devices, robotics, and IoT ecosystems.

• Why: There’s growing demand to connect language models to physical action—think humanoid robotics, logistics automation, and voice assistants for manufacturing.

• Insight: Voice-first interfaces for factory floors are becoming a quiet revolution in European manufacturing trials (Aagaard & Tucci, 2024).

⸻

5. AI as Creative Collaborator, Not Just Tool

• Trend: Beyond productivity, early adopters are embracing AI as a collaborative partner in music, design, film, and literature.

• Why: AI isn’t just a utility—it’s becoming part of the creative process itself.

• Insight: Studies find a positive psychological relationship between creators and generative AI—especially when AI is “voiced” or given a persona (Campus, 2023).

⸻

🚨 Why This Matters for Companies

➤ Talent Magnetism

• Attracting early adopters gives you a sandbox for rapid AI experimentation—turning your org into an idea incubator.

➤ Resilience in Disruption

• While most teams fear new tools, early adopters test them first and provide cultural scaffolding for others.

➤ Faster Competitive Cycles

• Firms that empower early adopters innovate faster and pivot earlier—this alone can add quarters of lead time over rivals (Chen et al., 2024).

➤ In-house Innovation Labs

• Early adopters often create internal tools before execs have time to schedule a vendor demo.

⸻

🧠 Final Thought

As early adopters begin building with AI that sees, hears, acts, and creates, the frontier of AI will shift from text to experience—and from automation to agency.

References

Aagaard, A., & Tucci, C. (2024). Pioneering new frontiers in value. Springer. Retrieved from https://books.google.com

Campus, M. (2023). AI for innovators—An exploratory study on the application of Artificial Intelligence as a supportive tool in the innovation process. University of Gothenburg. Retrieved from https://gupea.ub.gu.se

Chen, C. T., Chen, S. C., Khan, A., & Lim, M. K. (2024). Antecedents of big data analytics and artificial intelligence adoption on operational performance: The ChatGPT platform. Industrial Management & Data Systems. Retrieved from https://www.emerald.com

Gupta, V. (2024). An empirical evaluation of a generative artificial intelligence technology adoption model from entrepreneurs’ perspectives. Systems, 12(3), 103. Retrieved from https://www.mdpi.com/2079-8954/12/3/103

Haque, M. U., Dharmadasa, I., Sworna, Z. T., Rajapakse, R., & Ahmad, H. (2022). “I think this is the most disruptive technology”: Exploring sentiments of ChatGPT early adopters using Twitter data [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2212.05856

Companies racing to integrate artificial intelligence (AI) often face a bottleneck that’s not technical—it’s human. The true catalysts of innovation in the AI era are not just developers or data scientists, but early adopters of the newest technologies. These individuals don’t merely use tools; they reshape ecosystems.

Below is a research-backed guide to why hiring early adopters should be a C-suite priority—and what roles they thrive in.

💡 Top Characteristics of Early AI Adopters

· High Openness to Experience: Individuals and leaders who are curious, imaginative, and open to novel experiences are significantly more likely to explore and implement AI tools (Ibrahim et al., 2025).

· Technological Self-Efficacy: Confidence in using complex systems predicts proactive engagement with AI platforms, particularly among professionals in data-intensive roles (McElheran, Li, & Brynjolfsson, 2024).

· Innovator-Adopter Identity: Early adopters often self-identify with innovation-driven values and perceive AI as an extension of their personal and professional brand (Dale et al., 2021).

· Strategic Risk-Tolerance: Willingness to take calculated risks for potential high returns is common among organizational leaders who embrace AI early (Chintalapati, 2021).

· Values-Aligned Adoption: Ethical and value-driven leaders prioritize AI that aligns with organizational culture and stakeholder benefit, emphasizing responsible innovation (Ahituv & Hasgall, n.d.).

· Influence of AI Mindset: A belief in AI’s potential to augment rather than replace human intelligence fuels early adoption among professionals with a growth-oriented outlook (Ibrahim et al., 2025).

💼 Why Companies Should Hire Them

1. Accelerated Innovation

• Early adopters bring fresh thinking that pushes R&D, marketing, and ops teams to integrate AI faster and more effectively (Sipola et al., 2023).

2. Cultural Transformation Agents

• They influence organizational mindset by reducing resistance to AI and promoting a culture of experimentation (Hagemann, 2023).

3. Strategic Differentiation

• Firms with early adopters in key positions outperform late adopters in product development and market entry strategies (Chintalapati, 2021).

4. Economic Advantage

• Adoption leads to increased ROI through process automation, better decision-making, and customer insight extraction (Bhalerao et al., 2022).

📌 Ideal Roles for AI Early Adopters

To harness their strengths, position early adopters in:

Innovation Catalyst Roles

• Titles: AI Product Manager, Chief Innovation Officer, Director of Emerging Tech

• Function: Shape AI strategy, pilot emerging tools

Cross-Functional Integration

• Titles: Solutions Architect, Technical Evangelist, Change Manager

• Function: Translate AI capabilities across departments

Internal Influencers

• Titles: AI Trainer, UX Researcher with AI specialization

• Function: Drive adoption and usability across teams

Frontline Leadership

• Titles: Team Lead – AI Ops, Project Manager – Data Strategy

• Function: Embed AI in workflows, inspire teams to adapt

🧠 The Science Is Clear

The organizational impact of early adopters is not just theoretical—it’s empirically documented. Across sectors, early adopters consistently deliver faster learning cycles, more resilient operations, and higher adaptability in dynamic markets (Nafizah et al., 2024; Antony et al., 2024)

🧠 🧠 How to implement:

· Psychographic inquiry during recruitment: Recruitment and upskilling strategies should consider openness, tech-efficacy, and innovation alignment as key traits for internal champions of AI.

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References

Aagaard, A., & Tucci, C. (2024). Pioneering new frontiers in value. Springer. Retrieved from https://books.google.com

Antony, J., Sony, M., McDermott, O., & Furterer, S. (2023). How does performance vary between early and late adopters of Industry 4.0? A qualitative viewpoint. International Journal of Quality & Reliability Management. Retrieved from https://www.researchgate.net

Bhalerao, K., Kumar, A., & Kumar, A. (2022). A study of barriers and benefits of artificial intelligence adoption in small and medium enterprise. ResearchGate. Retrieved from https://www.researchgate.net

Campus, M. (2023). AI for innovators—An exploratory study on the application of Artificial Intelligence as a supportive tool in the innovation process. University of Gothenburg. Retrieved from https://gupea.ub.gu.se

Chen, C. T., Chen, S. C., Khan, A., & Lim, M. K. (2024). Antecedents of big data analytics and artificial intelligence adoption on operational performance: The ChatGPT platform. Industrial Management & Data Systems. Retrieved from https://www.emerald.com

Chintalapati, S. (2021). Early adopters to early majority—What’s driving the artificial intelligence and machine learning powered transformation in financial services. International Journal of Financial Research, 12(4), 43–53. https://doi.org/10.5430/IJFR.V12N4P43

Gupta, V. (2024). An empirical evaluation of a generative artificial intelligence technology adoption model from entrepreneurs’ perspectives. Systems, 12(3), 103. Retrieved from https://www.mdpi.com/2079-8954/12/3/103

Hagemann, K. (2023). AI Adoption in Early-Stage Tech Startups: An Exploratory Study [Doctoral dissertation, Unpublished]. ProQuest. Retrieved from https://search.proquest.com

McElheran, K., Li, J. F., & Brynjolfsson, E. (2024). AI adoption in America: Who, what, and where. Journal of Economics & Management Strategy. https://doi.org/10.1111/jems.12576

Nafizah, U. Y., Roper, S., & Mole, K. (2024). Estimating the innovation benefits of first-mover and second-mover strategies when micro-businesses adopt artificial intelligence and machine learning. Small Business Economics. Retrieved from https://link.springer.com/article/10.1007/s11187-023-00779-x

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Here’s how ChatGPT will likely support long-term AI adoption and momentum among late adopters:

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1. Personalized Onboarding Guidance

🧭 Late adopters often fear making mistakes. ChatGPT can act as a friendly onboarding assistant—offering real-time, conversational walkthroughs of features based on individual comfort levels and past tech usage. This builds trust.

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2. Just-in-Time Confidence Boosters

💬 Using behavioral nudges, ChatGPT can offer well-timed encouragement (e.g., “You just completed your first task—great job!”) to help users feel capable and competent early on, a key predictor of continued use. Confidence is a make-or-break factor for adoption among hesitant users.

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3. Cultural Translation Layer

🌍 ChatGPT can translate not just languages but technological metaphors—making abstract concepts (like cloud sync or tokenization) easier to understand. When tech language feels foreign or opaque, people disengage. Translation builds belonging.

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4. Embedded Habit Formation Coaching

⏳ ChatGPT can help structure tiny, repeatable actions around using the app, helping turn sporadic use into habit—“Want me to remind you each morning to log your meals?”. Habit formation reduces cognitive load.

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5. Safe Space for “Stupid Questions”

❓Late adopters often avoid support chats because they feel judged. A friendly, patient AI like ChatGPT offers a nonjudgmental space to ask anything, 24/7—even “What does this button do?”

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6. Emotional Friction Diffuser

🧠 ChatGPT can help users process tech frustration calmly and in a low-stakes, conversational tone (“Let’s fix that step by step”). For late adopters, emotional overwhelm is often the true barrier, not lack of features.

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7. Trusted Guide for Digital Skeptics

🛡️ ChatGPT can surface evidence-based answers to security, privacy, and legitimacy concerns—reducing the fear barrier that keeps late adopters from engaging with new platforms. Many late adopters aren’t tech-adverse, they’re risk adverse.

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8. Humanized Follow-up

📅 ChatGPT can simulate follow-up check-ins (e.g., “Want to see what new features were added this week?”) to keep users re-engaged. The tone mimics one of a friend.

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9. Contextualizing Benefits to Daily Life

🏠 Instead of vague feature lists, ChatGPT can help users connect the app to their real-life goals: “You said you want to spend less time budgeting—here’s how this app can save you 30 minutes a week.” Adoption increases when the “Why” is clear and deeply personal.

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10. “Show Me” Mode

🎯 ChatGPT offers real-time, side-by-side demos showing how AI can solve specific user problems like writing an email, summarizing a document, or meal planning. This works for late adopters because they don’t need to see what AI can do. This creates a sense of discovery (AHA moment) and builds trust.

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👀 Final Thought:

Late adopters aren’t resistant—they’re just cautious, overwhelmed, or underserved. In this space, we seek a starting point of smoothing the path from hesitation to habit.

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🔁 Curious how this can be applied to your app or user base? The Psych Lab is studying the intersection of behavioral science and technology adoption. Let’s connect.

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…Are Prompt Patterns Universal?

Not quite. But they are emotionally resonant.

Across the globe, people use AI tools like ChatGPT in rhythm with their daily lives—but those rhythms vary more than you might think. While there are shared arcs in usage (morning productivity, afternoon decision-making, evening entertainment, late-night introspection), cultural norms, religious observances, time zones, workweek structures, and even climate shape when and how people prompt.

Recent studies in digital ethnography and cross-cultural computing suggest that our cognitive states and emotional availability for digital interaction are not just personal—they're patterned by where, when, and how we live (Mark & Su, 2010; Sellen et al., 2009).

• In Nordic countries, Saturdays may skew more toward outdoor planning, home organization, and seasonal reflections.

• In parts of Asia, weekends might involve more family-centered scheduling, study routines, or structured learning even late into the evening.

• In South America, late-night creativity and social coordination stretch later into the night or early morning hours.

• In regions observing religious Sabbath or prayer rituals, prompts may peak before or after observance windows.

So while the emotional cadence of the day is often shared—morning ambition, afternoon activity, nighttime soul-searching—the specific content and timing shift culturally and contextually.

As AI interfaces become more deeply woven into daily life, understanding these patterns is insight into the socio-temporal scaffolding of human intention.

References

· Mark, G., & Su, N. M. (2010). Making infrastructure visible for nomadic work. *Pervasive and Mobile Computing, 6*(3), 312–323. https://doi.org/10.1016/j.pmcj.2009.11.004

· Sellen, A. J., Whittaker, S., & Harper, R. (2009). Designing for cognitive offloading: When does the computer let us forget? In *CHI '09: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems* (pp. 2271–2274). ACM. https://doi.org/10.1145/1518701.1519058

· Zhao, S., & Lindley, S. E. (2014). Curation through use: Understanding the personal value of social media. In *Proceedings of the SIGCHI Conference on Human Factors in Computing Systems* (pp. 2431–2440). https://doi.org/10.1145/2556288.2557291

📊 Behavioral Design in Action: How to Run the Fresh Start Experiment for AI Adoption

What if the timing of your AI app launch mattered as much as the technology itself? Behavioral science suggests it does. This experiment tests a simple idea: nudging users to engage with AI tools during natural “fresh start” moments—like the New Year or their birthday—can significantly improve long-term adoption.

In this hypothetical design, 600 participants are recruited and then divided into three randomized groups. The Control Group (CG) receives standard onboarding only. Focus Study (FS) Group A receives nudges at public temporal landmarks like New Year’s Day or fiscal Q1. FS Group B receives personalized nudges tied to birthdays, new jobs, or project starts. Track app usage over 90 days using metrics like login frequency, report generation, and feature adoption. Let’s take a deeper look:

Overview: Testing whether aligning AI app engagement nudges with personal or cultural “reset points” improves long-term usage.

Step-by-step:

1. Recruit participants across a diverse professional population.

2. Survey for personal milestones (birthdays, anniversaries, etc.) and record major calendar resets (Jan 1, Q1, etc.).

3. Develop nudges (e.g., “It’s a fresh quarter—let’s set your goals”) and automate delivery.

4. Monitor usage metrics for 30, 60, and 90 days.

5. Compare retention and engagement across groups.

The most impactful fresh start trigger? Research by Dai, Milkman, and Riis (2014) identifies the New Year as a particularly potent motivational reset point—marking both a cultural and psychological boundary that signals “a clean slate.” Aligning AI app nudges with the New Year taps into broad societal momentum and existing goal-setting behavior, increasing the odds that users will engage with and retain the technology. This makes Q1 campaigns a strategic opportunity for rolling out or relaunching digital tools requiring consistent interaction.

📚 APA References

• Dai, H., Milkman, K. L., & Riis, J. (2014). The fresh start effect: Temporal landmarks motivate aspirational behavior. Management Science, 60(10), 2563–2582. https://doi.org/10.2139/ssrn.2294674

• Duong, C. D. (2024, June 12). What makes for digital entrepreneurs? The role of AI-related drivers for nascent digital start-up activities. European Journal of Innovation Management. https://doi.org/10.1108/ejim-02-2024-0154

• Gupta, V., & Yang, H. (2024, January 5). Generative Artificial Intelligence (AI) Technology Adoption Model for Entrepreneurs: Case of ChatGPT. Journal of Global Information Management. https://doi.org/10.1080/10875301.2023.2300114